Journal articles: 'Robust functional data analysis'

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Locantore, N., J. S. Marron, D. G. Simpson, N. Tripoli, J. T. Zhang, K. L. Cohen, Graciela Boente, et al. "Robust principal component analysis for functional data." Test 8, no. 1 (June 1999): 1–73. http://dx.doi.org/10.1007/bf02595862.

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Sun, Jian, Haitao Liao, and Belle R. Upadhyaya. "A Robust Functional-Data-Analysis Method for Data Recovery in Multichannel Sensor Systems." IEEE Transactions on Cybernetics 44, no. 8 (August 2014): 1420–31. http://dx.doi.org/10.1109/tcyb.2013.2285876.

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Aaron, Catherine, Alejandro Cholaquidis, Ricardo Fraiman, and Badih Ghattas. "Multivariate and functional robust fusion methods for structured Big Data." Journal of Multivariate Analysis 170 (March 2019): 149–61. http://dx.doi.org/10.1016/j.jmva.2018.06.012.

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Yu, Yunqing. "Functional Principal Component Analysis: A Robust Method for Time-Series Phenotypic Data." Plant Physiology 183, no. 4 (August 2020): 1422–23. http://dx.doi.org/10.1104/pp.20.00797.

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Huang, Su-Yun, Yi-Ren Yeh, and Shinto Eguchi. "Robust Kernel Principal Component Analysis." Neural Computation 21, no. 11 (November 2009): 3179–213. http://dx.doi.org/10.1162/neco.2009.02-08-706.

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This letter discusses the robustness issue of kernel principal component analysis. A class of new robust procedures is proposed based on eigenvalue decomposition of weighted covariance. The proposed procedures will place less weight on deviant patterns and thus be more resistant to data contamination and model deviation. Theoretical influence functions are derived, and numerical examples are presented as well. Both theoretical and numerical results indicate that the proposed robust method outperforms the conventional approach in the sense of being less sensitive to outliers. Our robust method and results also apply to functional principal component analysis.

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De Haas, W. Bas, José Pedro Magalhães, Frans Wiering, and Remco C. Veltkamp. "Automatic Functional Harmonic Analysis." Computer Music Journal 37, no. 4 (December 2013): 37–53. http://dx.doi.org/10.1162/comj_a_00209.

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Music scholars have been studying tonal harmony intensively for centuries, yielding numerous theories and models. Unfortunately, a large number of these theories are formulated in a rather informal fashion and lack mathematical precision. In this article we present HarmTrace, a functional model of Western tonal harmony that builds on well-known theories of tonal harmony. In contrast to other approaches that remain purely theoretical, we present an implemented system that is evaluated empirically. Given a sequence of symbolic chord labels, HarmTrace automatically derives the harmonic relations between chords. For this, we use advanced functional programming techniques that are uniquely available in the Haskell programming language. We show that our system is fast, easy to modify and maintain, robust against noisy data, and that its harmonic analyses comply with Western tonal harmony theory.

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Cristani, M., A. Daducci, P. Farace, P. Marzola, V. Murino, A. Sbarbati, and U. Castellani. "DCE-MRI Data Analysis for Cancer Area Classification." Methods of Information in Medicine 48, no. 03 (2009): 248–53. http://dx.doi.org/10.3414/me9224.

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Summary Objectives: The paper aims at improving the support of medical researchers in the context of in-vivo cancer imaging. Morphological and functional parameters obtained by dynamic contrast-enhanced MRI (DCE-MRI) techniques are analyzed, which aim at investigating the development of tumor microvessels. The main contribution consists in proposing a machine learning methodology to segment automatically these MRI data, by isolating tumor areas with different meaning, in a histological sense. Methods: The proposed approach is based on a three-step procedure: i) robust feature extraction from raw time-intensity curves, ii) voxel segmentation, and iii) voxel classification based on a learning-by-example approach. In the first step, few robust features that compactly represent the response of the tissue to the DCE-MRI analysis are computed. The second step provides a segmentation based on the mean shift (MS) paradigm, which has recently shown to be robust and useful for different and heterogeneous clustering tasks. Finally, in the third step, a support vector machine (SVM) is trained to classify voxels according to the labels obtained by the clustering phase (i.e., each class corresponds to a cluster). Indeed, the SVM is able to classify new unseen subjects with the same kind of tumor. Results: Experiments on different subjects affected by the same kind of tumor evidence that the extracted regions by both the MS clustering and the SVM classifier exhibit a precise medical meaning, as carefully validated by the medical researchers. Moreover, our approach is more stable and robust than methods based on quantification of DCE-MRI data by means of pharmacokinetic models. Conclusions: The proposed method allows to analyze the DCE-MRI data more precisely and faster than previous automated or manual approaches.

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Beaty, Roger E., Yoed N. Kenett, Alexander P. Christensen, Monica D. Rosenberg, Mathias Benedek, Qunlin Chen, Andreas Fink, et al. "Robust prediction of individual creative ability from brain functional connectivity." Proceedings of the National Academy of Sciences 115, no. 5 (January 16, 2018): 1087–92. http://dx.doi.org/10.1073/pnas.1713532115.

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People’s ability to think creatively is a primary means of technological and cultural progress, yet the neural architecture of the highly creative brain remains largely undefined. Here, we employed a recently developed method in functional brain imaging analysis—connectome-based predictive modeling—to identify a brain network associated with high-creative ability, using functional magnetic resonance imaging (fMRI) data acquired from 163 participants engaged in a classic divergent thinking task. At the behavioral level, we found a strong correlation between creative thinking ability and self-reported creative behavior and accomplishment in the arts and sciences (r = 0.54). At the neural level, we found a pattern of functional brain connectivity related to high-creative thinking ability consisting of frontal and parietal regions within default, salience, and executive brain systems. In a leave-one-out cross-validation analysis, we show that this neural model can reliably predict the creative quality of ideas generated by novel participants within the sample. Furthermore, in a series of external validation analyses using data from two independent task fMRI samples and a large task-free resting-state fMRI sample, we demonstrate robust prediction of individual creative thinking ability from the same pattern of brain connectivity. The findings thus reveal a whole-brain network associated with high-creative ability comprised of cortical hubs within default, salience, and executive systems—intrinsic functional networks that tend to work in opposition—suggesting that highly creative people are characterized by the ability to simultaneously engage these large-scale brain networks.

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Wang, Shigang, Yongli Bi, and Yingsong Li. "Improvements on Robust Stability of Sampled-Data System with Long Time Delay." Mathematical Problems in Engineering 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/580768.

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This paper mainly studies the problem of the robust stability analysis for sampled-data system with long time delay. By constructing an improved Lyapunov-Krasovskii functional and employing some free weighting matrices, some new robust stability criteria can be established in terms of linear matrix inequalities. Furthermore, the proposed equivalent criterion eliminates the effect of free weighing matrices such that numbers of decision variables and computational burden are less than some existing results. A numerical example is also presented and compared with previously proposed algorithm to illustrate the feasibility and effectiveness of the developed results.

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Beckmann, Christian F., Marilena DeLuca, Joseph T. Devlin, and Stephen M. Smith. "Investigations into resting-state connectivity using independent component analysis." Philosophical Transactions of the Royal Society B: Biological Sciences 360, no. 1457 (May 29, 2005): 1001–13. http://dx.doi.org/10.1098/rstb.2005.1634.

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Inferring resting-state connectivity patterns from functional magnetic resonance imaging (fMRI) data is a challenging task for any analytical technique. In this paper, we review a probabilistic independent component analysis (PICA) approach, optimized for the analysis of fMRI data, and discuss the role which this exploratory technique can take in scientific investigations into the structure of these effects. We apply PICA to fMRI data acquired at rest, in order to characterize the spatio-temporal structure of such data, and demonstrate that this is an effective and robust tool for the identification of low-frequency resting-state patterns from data acquired at various different spatial and temporal resolutions. We show that these networks exhibit high spatial consistency across subjects and closely resemble discrete cortical functional networks such as visual cortical areas or sensory–motor cortex.

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Dorndorf, Alexander, Boris Kargoll, Jens-André Paffenholz, and Hamza Alkhatib. "Bayesian Robust Multivariate Time Series Analysis in Nonlinear Models with Autoregressive and t-Distributed Errors." Engineering Proceedings 5, no. 1 (June 28, 2021): 20. http://dx.doi.org/10.3390/engproc2021005020.

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Many geodetic measurement data can be modelled as a multivariate time series consisting of a deterministic (“functional”) model describing the trend, and a stochastic model of the correlated noise. These data are also often affected by outliers and their stochastic properties can vary significantly. The functional model of the time series is usually nonlinear regarding the trend parameters. To deal with these characteristics, a time series model, which can generally be explained as the additive combination of a multivariate, nonlinear regression model with multiple univariate, covariance-stationary autoregressive (AR) processes the white noise components of which obey independent, scaled t-distributions, was proposed by the authors in previous research papers. In this paper, we extend the aforementioned model to include prior knowledge regarding various model parameters, the information about which is often available in practical situations. We develop an algorithm based on Bayesian inference that provides a robust and reliable estimation of the functional parameters, the coefficients of the AR process and the parameters of the underlying t-distribution. We approximate the resulting posterior density using Markov chain Monte Carlo (MCMC) techniques consisting of a Metropolis-within-Gibbs algorithm.

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Kosiorowski, Daniel, Jerzy P. Rydlewski, and Zygmunt Zawadzki. "Functional Outliers Detection by the Example of Air Quality Monitoring." Przegląd Statystyczny 65, no. 1 (January 30, 2019): 83–100. http://dx.doi.org/10.5604/01.3001.0014.0528.

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Methods of functional outliers detection in functional setting have been discussed, i.e. shape outliers and magnitude outliers. Outliergram has been discussed, a tool for functional shape outliers detection. Robust adjusted functional boxplot has been discussed as well, a tool for functional magnitude outliers detection. „The elements of functional outliers analysis have been applied to air pollution data for Katowice and Kraków.”

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Hasenstab, Kyle, Catherine Sugar, Donatello Telesca, Shafali Jeste, and Damla Şentürk. "Robust functional clustering of ERP data with application to a study of implicit learning in autism." Biostatistics 17, no. 3 (February 4, 2016): 484–98. http://dx.doi.org/10.1093/biostatistics/kxw002.

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Abstract Motivated by a study on visual implicit learning in young children with Autism Spectrum Disorder (ASD), we propose a robust functional clustering (RFC) algorithm to identify subgroups within electroencephalography (EEG) data. The proposed RFC is an iterative algorithm based on functional principal component analysis, where cluster membership is updated via predictions of the functional trajectories obtained through a non-parametric random effects model. We consider functional data resulting from event-related potential (ERP) waveforms representing EEG time-locked to stimuli over the course of an implicit learning experiment, after applying a previously proposed meta-preprocessing step. This meta-preprocessing is designed to increase the low signal-to-noise ratio in the raw data and to mitigate the longitudinal changes in the ERP waveforms which characterize the nature and speed of learning. The resulting functional ERP components (peak amplitudes and latencies) inherently exhibit covariance heterogeneity due to low data quality over some stimuli inducing the averaging of different numbers of waveforms in sliding windows of the meta-preprocessing step. The proposed RFC algorithm incorporates this known covariance heterogeneity into the clustering algorithm, improving cluster quality, as illustrated in the data application and extensive simulation studies. ASD is a heterogeneous syndrome and identifying subgroups within ASD children is of interest for understanding the diverse nature of this complex disorder. Applications to the implicit learning paradigm identify subgroups within ASD and typically developing children with diverse learning patterns over the course of the experiment, which may inform clinical stratification of ASD.

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Rose, K. A., J. K. Summers, R. A. Cummins, and D. G. Heimbuch. "Analysis of Long-Term Ecological Data Using Categorical Time Series Regression." Canadian Journal of Fisheries and Aquatic Sciences 43, no. 12 (December 1, 1986): 2418–26. http://dx.doi.org/10.1139/f86-300.

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We propose a time series analysis method based on the use of categorized variables and ordinary least squares regression. It has several advantages over Box–Jenkins models and time series regression with continuous variables, including model specification based on ecological information, parsimonious representations of the functional forms of model terms and interactions, robust treatment of the high uncertainty associated with long-term ecological data, and interpretive features based on linear combinations of the regression coefficients. Aspects of model building, significance testing, and interpretation of results are discussed and illustrated with a fisheries example involving an annual measure of white perch (Morone americana) stock size in the Delaware River/Bay from 1929 to 1974. Variation in white perch dynamics is analyzed using the following explanatory variables: lagged values of stock, hydrographic variables (freshwater flow and water temperature), and pollution-related variables (sewage loading, dredging activity, and dissolved oxygen). Potential statistical problems with the new method involving multicollinearity, autocorrelated errors, and other violations of ordinary least squares are identified.

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Gerdes, S. Y., M. D. Scholle, J. W. Campbell, G. Balázsi, E. Ravasz, M. D. Daugherty, A. L. Somera, et al. "Experimental Determination and System Level Analysis of Essential Genes in Escherichia coli MG1655." Journal of Bacteriology 185, no. 19 (October 1, 2003): 5673–84. http://dx.doi.org/10.1128/jb.185.19.5673-5684.2003.

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ABSTRACT Defining the gene products that play an essential role in an organism's functional repertoire is vital to understanding the system level organization of living cells. We used a genetic footprinting technique for a genome-wide assessment of genes required for robust aerobic growth of Escherichia coli in rich media. We identified 620 genes as essential and 3,126 genes as dispensable for growth under these conditions. Functional context analysis of these data allows individual functional assignments to be refined. Evolutionary context analysis demonstrates a significant tendency of essential E. coli genes to be preserved throughout the bacterial kingdom. Projection of these data over metabolic subsystems reveals topologic modules with essential and evolutionarily preserved enzymes with reduced capacity for error tolerance.

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Wang, Ronghao, Yumou Qiu, Yuzhen Zhou, Zhikai Liang, and James C. Schnable. "A High-Throughput Phenotyping Pipeline for Image Processing and Functional Growth Curve Analysis." Plant Phenomics 2020 (July 14, 2020): 1–8. http://dx.doi.org/10.34133/2020/7481687.

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High-throughput phenotyping system has become more and more popular in plant science research. The data analysis for such a system typically involves two steps: plant feature extraction through image processing and statistical analysis for the extracted features. The current approach is to perform those two steps on different platforms. We develop the package “implant” in R for both robust feature extraction and functional data analysis. For image processing, the “implant” package provides methods including thresholding, hidden Markov random field model, and morphological operations. For statistical analysis, this package can produce nonparametric curve fitting with its confidence region for plant growth. A functional ANOVA model to test for the treatment and genotype effects on the plant growth dynamics is also provided.

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Zhang, Changzhu, Qijun Chen, and Jianbin Qiu. "Robust ℋ∞ filtering for vehicle sideslip angle estimation with sampled-data measurements." Transactions of the Institute of Measurement and Control 39, no. 7 (February 15, 2016): 1059–70. http://dx.doi.org/10.1177/0142331215627001.

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In this paper, the problem of vehicle sideslip angle estimation is studied based on a single-track model, and an approach to robust ℋ∞ filter design with sampled-data measurements is proposed. Considering the changes of the vehicle mass, the moment of inertia about the yaw axis and the nonlinear relationships between the road surface and tyres, the vehicle lateral dynamics are characterized by a system with parameter uncertainties, which belong to a given convex polytope. By utilizing an input delay approach, the filtering error system is transformed into a continuous-time system with time delay in the state. By introducing a Lyapunov–Krasovskii functional and a free weighting matrix technique, LMI-based conditions have been formulated for the stability analysis of the filtering error system and the existence of admissible filters, which ensure the filtering error system is asymptotically stable with a prescribed ℋ∞ disturbance attenuation level. Finally, some simulation results are provided to illustrate the effectiveness of the proposed method.

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Sheikhattar, Alireza, Sina Miran, Ji Liu, Jonathan B. Fritz, Shihab A. Shamma, Patrick O. Kanold, and Behtash Babadi. "Extracting neuronal functional network dynamics via adaptive Granger causality analysis." Proceedings of the National Academy of Sciences 115, no. 17 (April 9, 2018): E3869—E3878. http://dx.doi.org/10.1073/pnas.1718154115.

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Quantifying the functional relations between the nodes in a network based on local observations is a key challenge in studying complex systems. Most existing time series analysis techniques for this purpose provide static estimates of the network properties, pertain to stationary Gaussian data, or do not take into account the ubiquitous sparsity in the underlying functional networks. When applied to spike recordings from neuronal ensembles undergoing rapid task-dependent dynamics, they thus hinder a precise statistical characterization of the dynamic neuronal functional networks underlying adaptive behavior. We develop a dynamic estimation and inference paradigm for extracting functional neuronal network dynamics in the sense of Granger, by integrating techniques from adaptive filtering, compressed sensing, point process theory, and high-dimensional statistics. We demonstrate the utility of our proposed paradigm through theoretical analysis, algorithm development, and application to synthetic and real data. Application of our techniques to two-photon Ca2+ imaging experiments from the mouse auditory cortex reveals unique features of the functional neuronal network structures underlying spontaneous activity at unprecedented spatiotemporal resolution. Our analysis of simultaneous recordings from the ferret auditory and prefrontal cortical areas suggests evidence for the role of rapid top-down and bottom-up functional dynamics across these areas involved in robust attentive behavior.

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Torkamaneh, Davoud, Jérôme Laroche, and François Belzile. "Fast-GBS v2.0: an analysis toolkit for genotyping-by-sequencing data." Genome 63, no. 11 (November 2020): 577–81. http://dx.doi.org/10.1139/gen-2020-0077.

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Genotyping-by-sequencing (GBS) is a rapid, flexible, low-cost, and robust genotyping method that simultaneously discovers variants and calls genotypes within a broad range of samples. These characteristics make GBS an excellent tool for many applications and research questions from conservation biology to functional genomics in both model and non-model species. Continued improvement of GBS relies on a more comprehensive understanding of data analysis, development of fast and efficient bioinformatics pipelines, accurate missing data imputation, and active post-release support. Here, we present the second generation of Fast-GBS (v2.0) that offers several new options (e.g., processing paired-end reads and imputation of missing data) and features (e.g., summary statistics of genotypes) to improve the GBS data analysis process. The performance assessment analysis showed that Fast-GBS v2.0 outperformed other available analytical pipelines, such as GBS-SNP-CROP and Gb-eaSy. Fast-GBS v2.0 provides an analysis platform that can be run with different types of sequencing data, modest computational resources, and allows for missing-data imputation for various species in different contexts.

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Quadeer, Ahmed A., David Morales-Jimenez, and Matthew R. McKay. "RocaSec: a standalone GUI-based package for robust co-evolutionary analysis of proteins." Bioinformatics 36, no. 7 (December 4, 2019): 2262–63. http://dx.doi.org/10.1093/bioinformatics/btz890.

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Abstract Summary Patterns of mutational correlations, learnt from protein sequences, have been shown to be informative of co-evolutionary sectors that are tightly linked to functional and/or structural properties of proteins. Previously, we developed a statistical inference method, robust co-evolutionary analysis (RoCA), to reliably predict co-evolutionary sectors of proteins, while controlling for statistical errors caused by limited data. RoCA was demonstrated on multiple viral proteins, with the inferred sectors showing close correspondences with experimentally-known biochemical domains. To facilitate seamless use of RoCA and promote more widespread application to protein data, here we present a standalone cross-platform package ‘RocaSec’ which features an easy-to-use GUI. The package only requires the multiple sequence alignment of a protein for inferring the co-evolutionary sectors. In addition, when information on the protein biochemical domains is provided, RocaSec returns the corresponding statistical association between the inferred sectors and biochemical domains. Availability and implementation The RocaSec software is publicly available under the MIT License at https://github.com/ahmedaq/RocaSec. Supplementary information Supplementary data are available at Bioinformatics online.

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O’Packi, Paul, Rick Dubois, Nancy Armentrout, and Steve Bower. "Maine’s Approach to Data Warehousing for State Departments of Transportation." Transportation Research Record: Journal of the Transportation Research Board 1719, no. 1 (January 2000): 227–32. http://dx.doi.org/10.3141/1719-30.

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Most transportation agencies are faced with changing needs, challenges, and limited resources. State departments of transportation need tools to address these issues. One such solution combines data warehouse and geographic information systems (GIS) technology to allow easy access to reliable information for systemwide query, analysis, and reporting. To meet these challenges, to be more responsive, and to provide staff and managers with a better platform with which to deliver integrated transportation information to both internal and external customers, the Maine Department of Transportation (MeDOT) has turned to integrating data warehousing and GIS technologies. A detailed overview of MeDOT’s Transportation Information for Decision Enhancement (TIDE), a robust GIS-linked data warehouse, is given. A range of inherent technical issues involved in a project of this nature is discussed. The role that TIDE has played in breaking down the functional boundaries that have existed on both informational and technical fronts and how this robust tool facilitates the growth of agency integration also are discussed.

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Cheadle, Chris, Tonya Watkins, Jinshui Fan, Marc A. Williams, Steven Georas, John Hall, Antony Rosen, and Kathleen C. Barnes. "GSMA: Gene Set Matrix Analysis, An Automated Method for Rapid Hypothesis Testing of Gene Expression Data." Bioinformatics and Biology Insights 1 (January 2007): 117793220700100. http://dx.doi.org/10.1177/117793220700100003.

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Background Microarray technology has become highly valuable for identifying complex global changes in gene expression patterns. The assignment of functional information to these complex patterns remains a challenging task in effectively interpreting data and correlating results from across experiments, projects and laboratories. Methods which allow the rapid and robust evaluation of multiple functional hypotheses increase the power of individual researchers to data mine gene expression data more efficiently. Results We have developed (gene set matrix analysis) GSMA as a useful method for the rapid testing of group-wise up- or down-regulation of gene expression simultaneously for multiple lists of genes (gene sets) against entire distributions of gene expression changes (datasets) for single or multiple experiments. The utility of GSMA lies in its flexibility to rapidly poll gene sets related by known biological function or as designated solely by the end-user against large numbers of datasets simultaneously. Conclusions GSMA provides a simple and straightforward method for hypothesis testing in which genes are tested by groups across multiple datasets for patterns of expression enrichment.

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Lombardi, Angela, Sabina Tangaro, Roberto Bellotti, Alessandro Bertolino, Giuseppe Blasi, Giulio Pergola, Paolo Taurisano, and Cataldo Guaragnella. "A Novel Synchronization-Based Approach for Functional Connectivity Analysis." Complexity 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/7190758.

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Complex network analysis has become a gold standard to investigate functional connectivity in the human brain. Popular approaches for quantifying functional coupling between fMRI time series are linear zero-lag correlation methods; however, they might reveal only partial aspects of the functional links between brain areas. In this work, we propose a novel approach for assessing functional coupling between fMRI time series and constructing functional brain networks. A phase space framework is used to map couples of signals exploiting their cross recurrence plots (CRPs) to compare the trajectories of the interacting systems. A synchronization metric is extracted from the CRP to assess the coupling behavior of the time series. Since the functional communities of a healthy population are expected to be highly consistent for the same task, we defined functional networks of task-related fMRI data of a cohort of healthy subjects and applied a modularity algorithm in order to determine the community structures of the networks. The within-group similarity of communities is evaluated to verify whether such new metric is robust enough against noise. The synchronization metric is also compared with Pearson’s correlation coefficient and the detected communities seem to better reflect the functional brain organization during the specific task.

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Gracia-Tabuenca, Zeus, Juan Carlos Díaz-Patiño, Isaac Arelio, and Sarael Alcauter. "Topological Data Analysis Reveals Robust Alterations in the Whole-Brain and Frontal Lobe Functional Connectomes in Attention-Deficit/Hyperactivity Disorder." eneuro 7, no. 3 (April 21, 2020): ENEURO.0543–19.2020. http://dx.doi.org/10.1523/eneuro.0543-19.2020.

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Biondi, Biondo. "Velocity estimation by image-focusing analysis." GEOPHYSICS 75, no. 6 (November 2010): U49—U60. http://dx.doi.org/10.1190/1.3506505.

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Migration velocity can be estimated from seismic data by analyzing, focusing, and defocusing of residual-migrated images. The accuracy of these velocity estimates is limited by the inherent ambiguity between velocity and reflector curvature. However, velocity resolution improves when reflectors with different curvatures are present. Image focusing is measured by evaluating coherency across structural dips, in addition to coherency across aperture/azimuth angles. The inherent ambiguity between velocity and reflector curvature is directly tackled by introducing a curvature correction into the computation of the semblance functional that estimates image coherency. The resulting velocity estimator provides velocity estimates that are (1) unbiased by reflector curvature and (2) consistent with the velocity information that is routinely obtained by measuring coherency over aperture/azimuth angles. Applications to a 2D synthetic prestack data set and a 2D field prestack data set confirm that the proposed method provides consistent and unbiased velocity information. They also suggest that velocity estimates based on the new image-focusing semblance may be more robust and have higher resolution than estimates based on conventional semblance functionals. Applying the proposed method to zero-offset field data recorded in New York Harbor yields a velocity function that is consistent with available geologic information and clearly improves the focusing of the reflectors.

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Guerriero, Maria Luisa, Adam Corrigan, Aurélie Bornot, Mike Firth, Patrick O’Shea, Douglas Ross-Thriepland, and Samantha Peel. "Delivering Robust Candidates to the Drug Pipeline through Computational Analysis of Arrayed CRISPR Screens." SLAS DISCOVERY: Advancing the Science of Drug Discovery 25, no. 6 (May 12, 2020): 646–54. http://dx.doi.org/10.1177/2472555220921132.

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Genome-wide arrayed CRISPR screening is a powerful method for drug target identification as it enables exploration of the effect of individual gene perturbations using diverse highly multiplexed functional and phenotypic assays. Using high-content imaging, we can measure changes in biomarker expression, intracellular localization, and cell morphology. Here we present the computational pipeline we have developed to support the analysis and interpretation of arrayed CRISPR screens. This includes evaluating the quality of guide RNA libraries, performing image analysis, evaluating assay results quality, data processing, hit identification, ranking, visualization, and biological interpretation.

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Westfall, Daniel R., Sheeba A. Anteraper, Laura Chaddock-Heyman, Eric S. Drollette, Lauren B. Raine, Susan Whitfield-Gabrieli, Arthur F. Kramer, and Charles H. Hillman. "Resting-State Functional Connectivity and Scholastic Performance in Preadolescent Children: A Data-Driven Multivoxel Pattern Analysis (MVPA)." Journal of Clinical Medicine 9, no. 10 (October 2, 2020): 3198. http://dx.doi.org/10.3390/jcm9103198.

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Scholastic performance is the key metric by which schools measure student’s academic success, and it is important to understand the neural-correlates associated with greater scholastic performance. This study examines resting-state functional connectivity (RsFc) associated with scholastic performance (reading and mathematics) in preadolescent children (7–9 years) using an unbiased whole-brain connectome-wide multi-voxel pattern analysis (MVPA). MVPA revealed four clusters associated with reading composite score, these clusters were then used for whole-brain seed-based RsFc analysis. However, no such clusters were found for mathematics composite score. Post hoc analysis found robust associations between reading and RsFc dynamics with areas involved with the somatomotor, dorsal attention, ventral attention, limbic, frontoparietal, and default mode networks. These findings indicate that reading ability may be associated with a wide range of RsFc networks. Of particular interest, anticorrelations were observed between the default mode network and the somatomotor, dorsal attention, ventral attention, and frontoparietal networks. Previous research has demonstrated the importance of anticorrelations between the default mode network and frontoparietal network associated with cognition. These results extend the current literature exploring the role of network connectivity in scholastic performance of children.

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SHARMA, ALOK, SEIYA IMOTO, and SATORU MIYANO. "A BETWEEN-CLASS OVERLAPPING FILTER-BASED METHOD FOR TRANSCRIPTOME DATA ANALYSIS." Journal of Bioinformatics and Computational Biology 10, no. 05 (August 2012): 1250010. http://dx.doi.org/10.1142/s0219720012500102.

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Feature selection algorithms play a crucial role in identifying and discovering important genes for cancer classification. Feature selection algorithms can be broadly categorized into two main groups: filter-based methods and wrapper-based methods. Filter-based methods have been quite popular in the literature due to their many advantages, including computational efficiency, simplistic architecture, and an intuitively simple means of discovering biological and clinical aspects. However, these methods have limitations, and the classification accuracy of the selected genes is less accurate. In this paper, we propose a set of univariate filter-based methods using a between-class overlapping criterion. The proposed techniques have been compared with many other univariate filter-based methods using an acute leukemia dataset. The following properties have been examined: classification accuracy of the selected individual genes and the gene subsets; redundancy check among selected genes using ridge regression and LASSO methods; similarity and sensitivity analyses; functional analysis; and, stability analysis. A comprehensive experiment shows promising results for our proposed techniques. The univariate filter based methods using between-class overlapping criterion are accurate and robust, have biological significance, and are computationally efficient and easy to implement. Therefore, they are well suited for biological and clinical discoveries.

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Xie, Juan, Anjun Ma, Yu Zhang, Bingqiang Liu, Sha Cao, Cankun Wang, Jennifer Xu, Chi Zhang, and Qin Ma. "QUBIC2: a novel and robust biclustering algorithm for analyses and interpretation of large-scale RNA-Seq data." Bioinformatics 36, no. 4 (September 10, 2019): 1143–49. http://dx.doi.org/10.1093/bioinformatics/btz692.

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Abstract Motivation The biclustering of large-scale gene expression data holds promising potential for detecting condition-specific functional gene modules (i.e. biclusters). However, existing methods do not adequately address a comprehensive detection of all significant bicluster structures and have limited power when applied to expression data generated by RNA-Sequencing (RNA-Seq), especially single-cell RNA-Seq (scRNA-Seq) data, where massive zero and low expression values are observed. Results We present a new biclustering algorithm, QUalitative BIClustering algorithm Version 2 (QUBIC2), which is empowered by: (i) a novel left-truncated mixture of Gaussian model for an accurate assessment of multimodality in zero-enriched expression data, (ii) a fast and efficient dropouts-saving expansion strategy for functional gene modules optimization using information divergency and (iii) a rigorous statistical test for the significance of all the identified biclusters in any organism, including those without substantial functional annotations. QUBIC2 demonstrated considerably improved performance in detecting biclusters compared to other five widely used algorithms on various benchmark datasets from E.coli, Human and simulated data. QUBIC2 also showcased robust and superior performance on gene expression data generated by microarray, bulk RNA-Seq and scRNA-Seq. Availability and implementation The source code of QUBIC2 is freely available at https://github.com/OSU-BMBL/QUBIC2. Supplementary information Supplementary data are available at Bioinformatics online.

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Cardot, Hervé, Anne De Moliner, and Camelia Goga. "Conditional Bias Robust Estimation of the Total of Curve Data by Sampling in a Finite Population: An Illustration on Electricity Load Curves." Journal of Survey Statistics and Methodology 8, no. 3 (May 9, 2019): 453–82. http://dx.doi.org/10.1093/jssam/smz009.

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Abstract For marketing or power grid management purposes, many studies based on the analysis of total electricity consumption curves of groups of customers are now carried out by electricity companies. Aggregated totals or mean load curves are estimated using individual curves measured at fine time grid and collected according to some sampling design. Due to the skewness of the distribution of electricity consumptions, these samples often contain outlying curves which may have an important impact on the usual estimation procedures. We introduce several robust estimators of the total consumption curve which are not sensitive to such outlying curves. These estimators are based on the conditional bias approach and robust functional methods. We also derive mean square error estimators of these robust estimators, and finally, we evaluate and compare the performance of the suggested estimators on Irish electricity data.

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Baert, Annelies, Kris Villez, and Kathy Steppe. "Functional unfold principal component analysis for automatic plant-based stress detection in grapevine." Functional Plant Biology 39, no. 6 (2012): 519. http://dx.doi.org/10.1071/fp12007.

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Detection of drought stress is of great importance in grapevines because the plant’s water status strongly affects the quality of the grapes and hence, resulting wine. Measurements of stem diameter variations show promise for detecting drought stress, but they depend strongly on microclimatic changes. Tools for advanced data analysis might be helpful to distinguish drought from microclimate effects. To this end, we explored the possibilities of two data mining techniques: Unfold principal component analysis (UPCA) – an already established tool in several biotechnological domains – and functional unfold principal component analysis (FUPCA) – a newer technique combining functional data analysis with UPCA. With FUPCA, the original, multivariate time series of variables are first approximated by fitting the least-squares optimal linear combination of orthonomal basis functions. The resulting coefficients of these linear combinations are then subjected to UPCA. Both techniques were used to detect when the measured stem diameter variations in grapevine deviated from their normal conditions due to drought stress. Stress was detected with both UPCA and FUPCA days before visible symptoms appeared. However, FUPCA is less complex in the statistical sense and more robust than original UPCA modelling. Moreover, FUPCA can handle days with missing data, which is not possible with UPCA.

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Dubois, Albertine, Julien Dauguet, Anne-Sophie Herard, Laurent Besret, Edouard Duchesnay, Vincent Frouin, Philippe Hantraye, Gilles Bonvento, and Thierry Delzescaux. "Automated Three-Dimensional Analysis of Histological and Autoradiographic Rat Brain Sections: Application to an Activation Study." Journal of Cerebral Blood Flow & Metabolism 27, no. 10 (March 21, 2007): 1742–55. http://dx.doi.org/10.1038/sj.jcbfm.9600470.

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Besides the newly developed positron emission tomography scanners (microPET) dedicated to the in vivo functional study of small animals, autoradiography remains the reference technique widely used for functional brain imaging and the gold standard for the validation of in vivo results. The analysis of autoradiographic data is classically achieved in two dimensions (2D) using a section-by-section approach, is often limited to few sections and the delineation of the regions of interest to be analysed is directly performed on autoradiographic sections. In addition, such approach of analysis does not accommodate the possible anatomical shifts linked to dissymmetry associated with the sectioning process. This classic analysis is time-consuming, operator-dependent and can therefore lead to non-objective and non-reproducible results. In this paper, we have developed an automated and generic toolbox for processing of autoradiographic and corresponding histological rat brain sections based on a three-step approach, which involves: (1) an optimized digitization dealing with hundreds of autoradiographic and histological sections; (2) a robust reconstruction of the volumes based on a reliable registration method; and (3) an original 3D-geometry-based approach to analysis of anatomical and functional post-mortem data. The integration of the toolbox under a unified environment (in-house software BrainVISA, http://brainvisa.info ) with a graphic interface enabled a robust and operator-independent exploitation of the overall anatomical and functional information. We illustrated the substantial qualitative and quantitative benefits obtained by applying our methodology to an activation study (rats, n = 5, under unilateral visual stimulation).

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Omta, Wienand A., Roy G. van Heesbeen, Ian Shen, Jacob de Nobel, Desmond Robers, Lieke M. van der Velden, René H. Medema, et al. "Combining Supervised and Unsupervised Machine Learning Methods for Phenotypic Functional Genomics Screening." SLAS DISCOVERY: Advancing the Science of Drug Discovery 25, no. 6 (May 13, 2020): 655–64. http://dx.doi.org/10.1177/2472555220919345.

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There has been an increase in the use of machine learning and artificial intelligence (AI) for the analysis of image-based cellular screens. The accuracy of these analyses, however, is greatly dependent on the quality of the training sets used for building the machine learning models. We propose that unsupervised exploratory methods should first be applied to the data set to gain a better insight into the quality of the data. This improves the selection and labeling of data for creating training sets before the application of machine learning. We demonstrate this using a high-content genome-wide small interfering RNA screen. We perform an unsupervised exploratory data analysis to facilitate the identification of four robust phenotypes, which we subsequently use as a training set for building a high-quality random forest machine learning model to differentiate four phenotypes with an accuracy of 91.1% and a kappa of 0.85. Our approach enhanced our ability to extract new knowledge from the screen when compared with the use of unsupervised methods alone.

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Wang, Xin, Siyu He, Jian Li, Jun Wang, Chengyi Wang, Mingwei Wang, Danni He, et al. "pulseTD: RNA life cycle dynamics analysis based on pulse model of 4sU-seq time course sequencing data." PeerJ 8 (July 8, 2020): e9371. http://dx.doi.org/10.7717/peerj.9371.

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The life cycle of intracellular RNA mainly involves transcriptional production, splicing maturation and degradation processes. Their dynamic changes are termed as RNA life cycle dynamics (RLCD). It is still challenging for the accurate and robust identification of RLCD under unknow the functional form of RLCD. By using the pulse model, we developed an R package named pulseTD to identify RLCD by integrating 4sU-seq and RNA-seq data, and it provides flexible functions to capture continuous changes in RCLD rates. More importantly, it also can predict the trend of RNA transcription and expression changes in future time points. The pulseTD shows better accuracy and robustness than some other methods, and it is available on the GitHub repository (https://github.com/bioWzz/pulseTD_0.2.0).

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Palumbo, A. V., J. C. Schryver, M. W. Fields, C. E. Bagwell, J. Z. Zhou, T. Yan, X. Liu, and C. C. Brandt. "Coupling of Functional Gene Diversity and Geochemical Data from Environmental Samples." Applied and Environmental Microbiology 70, no. 11 (November 2004): 6525–34. http://dx.doi.org/10.1128/aem.70.11.6525-6534.2004.

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ABSTRACT Genomic techniques commonly used for assessing distributions of microorganisms in the environment often produce small sample sizes. We investigated artificial neural networks for analyzing the distributions of nitrite reductase genes (nirS and nirK) and two sets of dissimilatory sulfite reductase genes (dsrAB 1 and dsrAB 2) in small sample sets. Data reduction (to reduce the number of input parameters), cross-validation (to measure the generalization error), weight decay (to adjust model parameters to reduce generalization error), and importance analysis (to determine which variables had the most influence) were useful in developing and interpreting neural network models that could be used to infer relationships between geochemistry and gene distributions. A robust relationship was observed between geochemistry and the frequencies of genes that were not closely related to known dissimilatory sulfite reductase genes (dsrAB 2). Uranium and sulfate appeared to be the most related to distribution of two groups of these unusual dsrAB-related genes. For the other three groups, the distributions appeared to be related to pH, nickel, nonpurgeable organic carbon, and total organic carbon. The models relating the geochemical parameters to the distributions of the nirS, nirK, and dsrAB 1 genes did not generalize as well as the models for dsrAB 2. The data also illustrate the danger (generating a model that has a high generalization error) of not using a validation approach in evaluating the meaningfulness of the fit of linear or nonlinear models to such small sample sizes.

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Jackson, Heide, Michal Engelman, and Karen Bandeen-Roche. "Robust Respondents and Lost Limitations: The Implications of Nonrandom Missingness for the Estimation of Health Trajectories." Journal of Aging and Health 31, no. 4 (December 14, 2017): 685–708. http://dx.doi.org/10.1177/0898264317747079.

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Objective: We offer a strategy for quantifying the impact of mortality and attrition on inferences from later-life health trajectory models. Method: Using latent class growth analysis (LCGA), we identify functional limitation trajectory classes in the Health and Retirement Study. We compare results from complete case and full information maximum likelihood (FIML) analyses, and demonstrate a method for producing upper- and lower-bound estimates of the impact of attrition on results. Results: LCGA inferences vary substantially depending on the handling of missing data. For older adults who die during the follow-up period, the widely used FIML approach may underestimate functional limitations by up to 20%. Discussion: The most commonly used approaches to handling missing data likely underestimate the extent of poor health in aging populations. Although there is no single solution for nonrandom missingness, we show that bounding estimates can help analysts to better characterize patterns of health in later life.

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Potdar, Swapnil, Aleksandr Ianevski, John-Patrick Mpindi, Dmitrii Bychkov, Clément Fiere, Philipp Ianevski, Bhagwan Yadav, et al. "Breeze: an integrated quality control and data analysis application for high-throughput drug screening." Bioinformatics 36, no. 11 (March 2, 2020): 3602–4. http://dx.doi.org/10.1093/bioinformatics/btaa138.

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Abstract Summary High-throughput screening (HTS) enables systematic testing of thousands of chemical compounds for potential use as investigational and therapeutic agents. HTS experiments are often conducted in multi-well plates that inherently bear technical and experimental sources of error. Thus, HTS data processing requires the use of robust quality control procedures before analysis and interpretation. Here, we have implemented an open-source analysis application, Breeze, an integrated quality control and data analysis application for HTS data. Furthermore, Breeze enables a reliable way to identify individual drug sensitivity and resistance patterns in cell lines or patient-derived samples for functional precision medicine applications. The Breeze application provides a complete solution for data quality assessment, dose–response curve fitting and quantification of the drug responses along with interactive visualization of the results. Availability and implementation The Breeze application with video tutorial and technical documentation is accessible at https://breeze.fimm.fi; the R source code is publicly available at https://github.com/potdarswapnil/Breeze under GNU General Public License v3.0. Contact swapnil.potdar@helsinki.fi Supplementary information Supplementary data are available at Bioinformatics online.

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Gorges, Martin, Hans-Peter Müller, Albert C. Ludolph, Volker Rasche, and Jan Kassubek. "Intrinsic Functional Connectivity Networks in Healthy Elderly Subjects: A Multiparametric Approach with Structural Connectivity Analysis." BioMed Research International 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/947252.

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Intrinsic functional connectivity magnetic resonance imaging (iFCMRI) provides an encouraging approach for mapping large-scale intrinsic connectivity networks (ICNs) in the “resting” brain. Structural connections as measured by diffusion tensor imaging (DTI) are a major constraint on the identified ICNs. This study aimed at the combined investigation of ten well-defined ICNs in healthy elderly subjects at single subject level as well as at the group level, together with the underlying structural connectivity. IFCMRI and DTI data were acquired in twelve subjects (68 ± 7 years) at a 3T scanner and were studied using thetensor imaging and fiber trackingsoftware package. The seed-based iFCMRI analysis approach was comprehensively performed with DTI analysis, following standardized procedures including an 8-step processing of iFCMRI data. Our findings demonstrated robust ICNs at the single subject level and conclusive brain maps at the group level in the healthy elderly sample, supported by the complementary fiber tractography. The findings demonstrated here provide a methodological framework for future comparisons of pathological (e.g., neurodegenerative) conditions with healthy controls on the basis of multiparametric functional connectivity mapping.

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Aggogeri, Francesco, Nicola Pellegrini, and Riccardo Adamini. "Functional Design in Rehabilitation: Modular Mechanisms for Ankle Complex." Applied Bionics and Biomechanics 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/9707801.

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This paper is aimed at presenting an innovative ankle rehabilitation device based on a parallel mechanism. A functional analysis and design are described to obtain a device able to guarantee ankle movement while patient’s body remains stationary. Human ankle is a challenging context where a series of joints are highly integrated. The proposed rehabilitation device permits a patient with walking defects to improve his or her gait. The research focuses on plantar-flexion-dorsiflexion movement. The robust design starts from an accurate modelling of ankle movements during walking, assessing motion data from healthy individuals and patients. The kinematics analysis and functional evaluations lead the study and development of the articulated system. In particular, results of simulations support the effectiveness of the current design. A 3D prototype is presented highlighting that the ankle motion is successfully demonstrated.

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Blanchard, Tommy C., Steven T. Piantadosi, and Benjamin Y. Hayden. "Robust mixture modeling reveals category-free selectivity in reward region neuronal ensembles." Journal of Neurophysiology 119, no. 4 (April 1, 2018): 1305–18. http://dx.doi.org/10.1152/jn.00808.2017.

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Classification of neurons into clusters based on their response properties is an important tool for gaining insight into neural computations. However, it remains unclear to what extent neurons fall naturally into discrete functional categories. We developed a Bayesian method that models the tuning properties of neural populations as a mixture of multiple types of task-relevant response patterns. We applied this method to data from several cortical and striatal regions in economic choice tasks. In all cases, neurons fell into only two clusters: one multiple-selectivity cluster containing all cells driven by task variables of interest and another of no selectivity for those variables. The single cluster of task-sensitive cells argues against robust categorical tuning in these areas. The no-selectivity cluster was unanticipated and raises important questions about what distinguishes these neurons and what role they play. Moreover, the ability to formally identify these nonselective cells allows for more accurate measurement of ensemble effects by excluding or appropriately down-weighting them in analysis. Our findings provide a valuable tool for analysis of neural data, challenge simple categorization schemes previously proposed for these regions, and place useful constraints on neurocomputational models of economic choice and control. NEW & NOTEWORTHY We present a Bayesian method for formally detecting whether a population of neurons can be naturally classified into clusters based on their response tuning properties. We then examine several data sets of reward system neurons for variables and find in all cases that neurons can be classified into only two categories: a functional class and a non-task-driven class. These results provide important constraints for neural models of the reward system.

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de Lastic, Hector-Xavier, Irene Liampa, Alexandros G. Georgakilas, Michalis Zervakis, and Aristotelis Chatziioannou. "Entropic Ranks: A Methodology for Enhanced, Threshold-Free, Information-Rich Data Partition and Interpretation." Applied Sciences 10, no. 20 (October 12, 2020): 7077. http://dx.doi.org/10.3390/app10207077.

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Background: Here, we propose a threshold-free selection method for the identification of differentially expressed features based on robust, non-parametric statistics, ensuring independence from the statistical distribution properties and broad applicability. Such methods could adapt to different initial data distributions, contrary to statistical techniques, based on fixed thresholds. This work aims to propose a methodology, which automates and standardizes the statistical selection, through the utilization of established measures like that of entropy, already used in information retrieval from large biomedical datasets, thus departing from classical fixed-threshold based methods, relying in arbitrary p-value and fold change values as selection criteria, whose efficacy also depends on degree of conformity to parametric distributions,. Methods: Our work extends the rank product (RP) methodology with a neutral selection method of high information-extraction capacity. We introduce the calculation of the RP entropy of the distribution, to isolate the features of interest by their contribution to its information content. Goal is a methodology of threshold-free identification of the differentially expressed features, which are highly informative about the phenomenon under study. Conclusions: Applying the proposed method on microarray (transcriptomic and DNA methylation) and RNAseq count data of varying sizes and noise presence, we observe robust convergence for the different parameterizations to stable cutoff points. Functional analysis through BioInfoMiner and EnrichR was used to evaluate the information potency of the resulting feature lists. Overall, the derived functional terms provide a systemic description highly compatible with the results of traditional statistical hypothesis testing techniques. The methodology behaves consistently across different data types. The feature lists are compact and rich in information, indicating phenotypic aspects specific to the tissue and biological phenomenon investigated. Selection by information content measures efficiently addresses problems, emerging from arbitrary thresh-holding, thus facilitating the full automation of the analysis.

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Li, Ziyi, Changgee Chang, Suprateek Kundu, and Qi Long. "Bayesian generalized biclustering analysis via adaptive structured shrinkage." Biostatistics 21, no. 3 (December 31, 2018): 610–24. http://dx.doi.org/10.1093/biostatistics/kxy081.

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Summary Biclustering techniques can identify local patterns of a data matrix by clustering feature space and sample space at the same time. Various biclustering methods have been proposed and successfully applied to analysis of gene expression data. While existing biclustering methods have many desirable features, most of them are developed for continuous data and few of them can efficiently handle -omics data of various types, for example, binomial data as in single nucleotide polymorphism data or negative binomial data as in RNA-seq data. In addition, none of existing methods can utilize biological information such as those from functional genomics or proteomics. Recent work has shown that incorporating biological information can improve variable selection and prediction performance in analyses such as linear regression and multivariate analysis. In this article, we propose a novel Bayesian biclustering method that can handle multiple data types including Gaussian, Binomial, and Negative Binomial. In addition, our method uses a Bayesian adaptive structured shrinkage prior that enables feature selection guided by existing biological information. Our simulation studies and application to multi-omics datasets demonstrate robust and superior performance of the proposed method, compared to other existing biclustering methods.

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Li, Liang, Bin Yan, Li Tong, Linyuan Wang, and Jianxin Li. "Incremental Activation Detection for Real-Time fMRI Series Using Robust Kalman Filter." Computational and Mathematical Methods in Medicine 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/759805.

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Real-time functional magnetic resonance imaging (rt-fMRI) is a technique that enables us to observe human brain activations in real time. However, some unexpected noises that emerged in fMRI data collecting, such as acute swallowing, head moving and human manipulations, will cause much confusion and unrobustness for the activation analysis. In this paper, a new activation detection method for rt-fMRI data is proposed based on robust Kalman filter. The idea is to add a variation to the extended kalman filter to handle the additional sparse measurement noise and a sparse noise term to the measurement update step. Hence, the robust Kalman filter is designed to improve the robustness for the outliers and can be computed separately for each voxel. The algorithm can compute activation maps on each scan within a repetition time, which meets the requirement for real-time analysis. Experimental results show that this new algorithm can bring out high performance in robustness and in real-time activation detection.

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Holdsworth, Clovia I., Michael C. Bowyer, Chris Lennard, and Adam McCluskey. "Formulation of Cocaine-Imprinted Polymers Utilizing Molecular Modelling and NMR Analysis." Australian Journal of Chemistry 58, no. 5 (2005): 315. http://dx.doi.org/10.1071/ch04138.

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Molecular imprinted polymers (MIPs) have distinctive features that make them attractive as an inexpensive, reusable, and robust field-based detection system for illicit substances. Optimizing MIP performance is traditionally attained by the synthesis and evaluation of a plethora of individual formulations. A non-covalently imprinted polymer for cocaine has been prepared using a commercially available molecular modelling package (Spartan 02) to predict energetically favourable monomer–template interactions between the target (T) and two different functional monomers (FM)—methacrylic acid (MAA) and 4-vinylpyridine (4VP). NMR studies undertaken to assess target–monomer behaviour in solution were in good agreement with the computational data. MIPs involving three target-to-functional monomer ratios (1 : 2, 1 : 6, and 1 : 14) were prepared and evaluated. Target rebinding was found to be most favourable in the 1 : 2 formulation with a target-selective binding of 0.48 ppm and an imprinting factor (I) of 2.8 obtained for 10 mg of test polymer.

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Liu, Chao, Basel Abu-Jamous, Elvira Brattico, and Asoke K. Nandi. "Towards Tunable Consensus Clustering for Studying Functional Brain Connectivity During Affective Processing." International Journal of Neural Systems 27, no. 02 (December 28, 2016): 1650042. http://dx.doi.org/10.1142/s0129065716500428.

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In the past decades, neuroimaging of humans has gained a position of status within neuroscience, and data-driven approaches and functional connectivity analyses of functional magnetic resonance imaging (fMRI) data are increasingly favored to depict the complex architecture of human brains. However, the reliability of these findings is jeopardized by too many analysis methods and sometimes too few samples used, which leads to discord among researchers. We propose a tunable consensus clustering paradigm that aims at overcoming the clustering methods selection problem as well as reliability issues in neuroimaging by means of first applying several analysis methods (three in this study) on multiple datasets and then integrating the clustering results. To validate the method, we applied it to a complex fMRI experiment involving affective processing of hundreds of music clips. We found that brain structures related to visual, reward, and auditory processing have intrinsic spatial patterns of coherent neuroactivity during affective processing. The comparisons between the results obtained from our method and those from each individual clustering algorithm demonstrate that our paradigm has notable advantages over traditional single clustering algorithms in being able to evidence robust connectivity patterns even with complex neuroimaging data involving a variety of stimuli and affective evaluations of them. The consensus clustering method is implemented in the R package “UNCLES” available on http://cran.r-project.org/web/packages/UNCLES/index.html .

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Marsden, Russell L., Juan A. G. Ranea, Antonio Sillero, Oliver Redfern, Corin Yeats, Michael Maibaum, David Lee, et al. "Exploiting protein structure data to explore the evolution of protein function and biological complexity." Philosophical Transactions of the Royal Society B: Biological Sciences 361, no. 1467 (February 2006): 425–40. http://dx.doi.org/10.1098/rstb.2005.1801.

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New directions in biology are being driven by the complete sequencing of genomes, which has given us the protein repertoires of diverse organisms from all kingdoms of life. In tandem with this accumulation of sequence data, worldwide structural genomics initiatives, advanced by the development of improved technologies in X-ray crystallography and NMR, are expanding our knowledge of structural families and increasing our fold libraries. Methods for detecting remote sequence similarities have also been made more sensitive and this means that we can map domains from these structural families onto genome sequences to understand how these families are distributed throughout the genomes and reveal how they might influence the functional repertoires and biological complexities of the organisms. We have used robust protocols to assign sequences from completed genomes to domain structures in the CATH database, allowing up to 60% of domain sequences in these genomes, depending on the organism, to be assigned to a domain family of known structure. Analysis of the distribution of these families throughout bacterial genomes identified more than 300 universal families, some of which had expanded significantly in proportion to genome size. These highly expanded families are primarily involved in metabolism and regulation and appear to make major contributions to the functional repertoire and complexity of bacterial organisms. When comparisons are made across all kingdoms of life, we find a smaller set of universal domain families (approx. 140), of which families involved in protein biosynthesis are the largest conserved component. Analysis of the behaviour of other families reveals that some (e.g. those involved in metabolism, regulation) have remained highly innovative during evolution, making it harder to trace their evolutionary ancestry. Structural analyses of metabolic families provide some insights into the mechanisms of functional innovation, which include changes in domain partnerships and significant structural embellishments leading to modulation of active sites and protein interactions.

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Federico, Antonio, Angela Serra, My Kieu Ha, Pekka Kohonen, Jang-Sik Choi, Irene Liampa, Penny Nymark, et al. "Transcriptomics in Toxicogenomics, Part II: Preprocessing and Differential Expression Analysis for High Quality Data." Nanomaterials 10, no. 5 (May 8, 2020): 903. http://dx.doi.org/10.3390/nano10050903.

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Preprocessing of transcriptomics data plays a pivotal role in the development of toxicogenomics-driven tools for chemical toxicity assessment. The generation and exploitation of large volumes of molecular profiles, following an appropriate experimental design, allows the employment of toxicogenomics (TGx) approaches for a thorough characterisation of the mechanism of action (MOA) of different compounds. To date, a plethora of data preprocessing methodologies have been suggested. However, in most cases, building the optimal analytical workflow is not straightforward. A careful selection of the right tools must be carried out, since it will affect the downstream analyses and modelling approaches. Transcriptomics data preprocessing spans across multiple steps such as quality check, filtering, normalization, batch effect detection and correction. Currently, there is a lack of standard guidelines for data preprocessing in the TGx field. Defining the optimal tools and procedures to be employed in the transcriptomics data preprocessing will lead to the generation of homogeneous and unbiased data, allowing the development of more reliable, robust and accurate predictive models. In this review, we outline methods for the preprocessing of three main transcriptomic technologies including microarray, bulk RNA-Sequencing (RNA-Seq), and single cell RNA-Sequencing (scRNA-Seq). Moreover, we discuss the most common methods for the identification of differentially expressed genes and to perform a functional enrichment analysis. This review is the second part of a three-article series on Transcriptomics in Toxicogenomics.

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Cai, Jinlu, Henry L. Keen, Curt D. Sigmund, and Thomas L. Casavant. "Coex-Rank: An approach incorporating co-expression information for combined analysis of microarray data." Journal of Integrative Bioinformatics 9, no. 1 (March 1, 2012): 32–43. http://dx.doi.org/10.1515/jib-2012-208.

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Summary Microarrays have been widely used to study differential gene expression at the genomic level. They can also provide genome-wide co-expression information. Biologically related datasets from independent studies are publicly available, which requires robust combined approaches for integration and validation. Previously, meta-analysis has been adopted to solve this problem.As an alternative to meta-analysis, for microarray data with high similarity in biological experimental design, a more direct combined approach is possible. Gene-level normalization across datasets is motivated by the different scale and distribution of data due to separate origins. However, there has been limited discussion about this point in the past. Here we describe a combined approach for microarray analysis, including gene-level normalization and Coex-Rank approach. After normalization, a linear modeling process is used to identify lists of differentially expressed genes. The Coex-Rank approach incorporates co-expression information into a rank-aggregation procedure. We applied this computational approach to our data, which illustrated an improvement in statistical power and a complementary advantage of the Coex-Rank approach from a biological perspective.Our combined approach for microarray data analysis (Coex-rank) is based on normalization, which is naturally driven. The Coex-rank process not only takes advantage of merging the power of multiple methods regarding normalization but also assists in the discovery of functional clusters of genes.

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Petralia, Maria Cristina, Rosella Ciurleo, Andrea Saraceno, Manuela Pennisi, Maria Sofia Basile, Paolo Fagone, Placido Bramanti, Ferdinando Nicoletti, and Eugenio Cavalli. "Meta-Analysis of Transcriptomic Data of Dorsolateral Prefrontal Cortex and of Peripheral Blood Mononuclear Cells Identifies Altered Pathways in Schizophrenia." Genes 11, no. 4 (April 3, 2020): 390. http://dx.doi.org/10.3390/genes11040390.

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Schizophrenia (SCZ) is a psychiatric disorder characterized by both positive and negative symptoms, including cognitive dysfunction, decline in motivation, delusion and hallucinations. Antipsychotic agents are currently the standard of care treatment for SCZ. However, only about one-third of SCZ patients respond to antipsychotic medications. In the current study, we have performed a meta-analysis of publicly available whole-genome expression datasets on Brodmann area 46 of the brain dorsolateral prefrontal cortex in order to prioritize potential pathways underlying SCZ pathology. Moreover, we have evaluated whether the differentially expressed genes in SCZ belong to specific subsets of cell types. Finally, a cross-tissue comparison at both the gene and functional level was performed by analyzing the transcriptomic pattern of peripheral blood mononuclear cells of SCZ patients. Our study identified a robust disease-specific set of dysfunctional biological pathways characterizing SCZ patients that could in the future be exploited as potential therapeutic targets.

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Basit, Abdul, Saqib Ali Khan, Waqas Tariq Toor, Naeem Maroof, Muhammad Saadi, and Atif Ali Khan. "A Novel Dissimilarity of Activity Biomarker and Functional Connectivity Analysis for the Epilepsy Diagnosis." Symmetry 11, no. 8 (August 2, 2019): 979. http://dx.doi.org/10.3390/sym11080979.

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Epilepsy is a central nervous system disorder that results in asymmetries of brain regional activation and connectivity patterns. The detection of these abnormalities is oftentimes challenging and requires identification of robust bio-markers that are representative of disease activity. Functional Magnetic Resonance Imaging (fMRI) is one of the several methods that can be used to detect such bio-markers. fMRI has a high spatial resolution which makes it a suitable candidate for designing computational methods for computer-aided biomarker discovery. In this paper, we present a computational framework for analyzing fMRI data consisting of 100 epileptic and 80 healthy patients, with an overall goal to produce a novel bio-marker that is predictive of epilepsy. The proposed method is primarily based on Dissimilarity of Activity (DoA) analysis. We demonstrate that the bio-marker presented in this study can be used to capture asymmetries in activities by detecting any abnormalities in Blood Oxygenated Level Dependent (BOLD) signal. In order to represent all asymmetries (of connectivity and activation patterns), we used functional connectivity analysis (FCA) in conjunction with DoA to find underlying connectivity patterns of the regions. Subsequently, these biomarkers were used to train a Support Vector Machine (SVM) classifier that was able to distinguish between healthy and epileptic patients with 87.8% accuracy. These results demonstrate the applicability of computer-aided methods in complex disease diagnosis by simply utilizing the existing data. With the advent of all modern sensing and imaging techniques, the use of intelligent algorithms and advanced computational methods are increasingly becoming the future of computer-aided diagnosis.

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Journal articles on the topic 'Robust functional data analysis'

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