Relevant bibliographies by topics / Bioinformatics predictions

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Bioinformatics predictions'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Bioinformatics predictions"

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Vangaveti, Sweta, Thom Vreven, Yang Zhang, and Zhiping Weng. "Integrating ab initio and template-based algorithms for protein–protein complex structure prediction." Bioinformatics 36, no. 3 (2019): 751–57. http://dx.doi.org/10.1093/bioinformatics/btz623.

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Abstract Motivation Template-based and template-free methods have both been widely used in predicting the structures of protein–protein complexes. Template-based modeling is effective when a reliable template is available, while template-free methods are required for predicting the binding modes or interfaces that have not been previously observed. Our goal is to combine the two methods to improve computational protein–protein complex structure prediction. Results Here, we present a method to identify and combine high-confidence predictions of a template-based method (SPRING) with a template-free method (ZDOCK). Cross-validated using the protein–protein docking benchmark version 5.0, our method (ZING) achieved a success rate of 68.2%, outperforming SPRING and ZDOCK, with success rates of 52.1% and 35.9% respectively, when the top 10 predictions were considered per test case. In conclusion, a statistics-based method that evaluates and integrates predictions from template-based and template-free methods is more successful than either method independently. Availability and implementation ZING is available for download as a Github repository (https://github.com/weng-lab/ZING.git). Supplementary information Supplementary data are available at Bioinformatics online.
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Sommer, I., J. Rahnenfuhrer, F. S. Domingues, U. de Lichtenberg, and T. Lengauer. "Predicting protein structure classes from function predictions." Bioinformatics 20, no. 5 (2004): 770–76. http://dx.doi.org/10.1093/bioinformatics/btg483.

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Wang, Debby D., Haoran Xie, and Hong Yan. "Proteo-chemometrics interaction fingerprints of protein–ligand complexes predict binding affinity." Bioinformatics 37, no. 17 (2021): 2570–79. http://dx.doi.org/10.1093/bioinformatics/btab132.

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Abstract Motivation Reliable predictive models of protein–ligand binding affinity are required in many areas of biomedical research. Accurate prediction based on current descriptors or molecular fingerprints (FPs) remains a challenge. We develop novel interaction FPs (IFPs) to encode protein–ligand interactions and use them to improve the prediction. Results Proteo-chemometrics IFPs (PrtCmm IFPs) formed by combining extended connectivity fingerprints (ECFPs) with the proteo-chemometrics concept. Combining PrtCmm IFPs with machine-learning models led to efficient scoring models, which were validated on the PDBbind v2019 core set and CSAR-HiQ sets. The PrtCmm IFP Score outperformed several other models in predicting protein–ligand binding affinities. Besides, conventional ECFPs were simplified to generate new IFPs, which provided consistent but faster predictions. The relationship between the base atom properties of ECFPs and the accuracy of predictions was also investigated. Availability PrtCmm IFP has been implemented in the IFP Score Toolkit on github (https://github.com/debbydanwang/IFPscore). Supplementary information Supplementary data are available at Bioinformatics online.
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Magnusson, Rasmus, and Mika Gustafsson. "LiPLike: towards gene regulatory network predictions of high certainty." Bioinformatics 36, no. 8 (2020): 2522–29. http://dx.doi.org/10.1093/bioinformatics/btz950.

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Abstract Motivation High correlation in expression between regulatory elements is a persistent obstacle for the reverse-engineering of gene regulatory networks. If two potential regulators have matching expression patterns, it becomes challenging to differentiate between them, thus increasing the risk of false positive identifications. Results To allow for gene regulation predictions of high confidence, we propose a novel method, the Linear Profile Likelihood (LiPLike), that assumes a regression model and iteratively searches for interactions that cannot be replaced by a linear combination of other predictors. To compare the performance of LiPLike with other available inference methods, we benchmarked LiPLike using three independent datasets from the Dialogue on Reverse Engineering Assessment and Methods 5 (DREAM5) network inference challenge. We found that LiPLike could be used to stratify predictions of other inference tools, and when applied to the predictions of DREAM5 participants, we observed an average improvement in accuracy of >140% compared to individual methods. Furthermore, LiPLike was able to independently predict networks better than all DREAM5 participants when applied to biological data. When predicting the Escherichia coli network, LiPLike had an accuracy of 0.38 for the top-ranked 100 interactions, whereas the corresponding DREAM5 consensus model yielded an accuracy of 0.11. Availability and implementation We made LiPLike available to the community as a Python toolbox, available at https://gitlab.com/Gustafsson-lab/liplike. We believe that LiPLike will be used for high confidence predictions in studies where individual model interactions are of high importance, and to remove false positive predictions made by other state-of-the-art gene–gene regulation prediction tools. Supplementary information Supplementary data are available at Bioinformatics online.
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Liu, Zhi-Ping. "Predicting lncRNA-protein Interactions by Machine Learning Methods: A Review." Current Bioinformatics 15, no. 8 (2021): 831–40. http://dx.doi.org/10.2174/1574893615666200224095925.

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In this work, a review of predicting lncRNA-protein interactions by bioinformatics methods is provided with a focus on machine learning. Firstly, a computational framework for predicting lncRNA-protein interactions is presented. Then, the currently available data resources for the predictions have been listed. The existing methods will be reviewed by introducing their crucial steps in the prediction framework. The key functions of lncRNA, e.g., mediator on transcriptional regulation, are often involved in interacting with proteins. The interactions with proteins provide a tunnel of leveraging the molecular cooperativity for fulfilling crucial functions. Thus, the important directions in bioinformatics have been highlighted for identifying essential lncRNA-protein interactions and deciphering the dysfunctional importance of lncRNA, especially in carcinogenesis.
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Xu, Gang, Qinghua Wang, and Jianpeng Ma. "OPUS-TASS: a protein backbone torsion angles and secondary structure predictor based on ensemble neural networks." Bioinformatics 36, no. 20 (2020): 5021–26. http://dx.doi.org/10.1093/bioinformatics/btaa629.

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Abstract Motivation Predictions of protein backbone torsion angles (ϕ and ψ) and secondary structure from sequence are crucial subproblems in protein structure prediction. With the development of deep learning approaches, their accuracies have been significantly improved. To capture the long-range interactions, most studies integrate bidirectional recurrent neural networks into their models. In this study, we introduce and modify a recently proposed architecture named Transformer to capture the interactions between the two residues theoretically with arbitrary distance. Moreover, we take advantage of multitask learning to improve the generalization of neural network by introducing related tasks into the training process. Similar to many previous studies, OPUS-TASS uses an ensemble of models and achieves better results. Results OPUS-TASS uses the same training and validation sets as SPOT-1D. We compare the performance of OPUS-TASS and SPOT-1D on TEST2016 (1213 proteins) and TEST2018 (250 proteins) proposed in the SPOT-1D paper, CASP12 (55 proteins), CASP13 (32 proteins) and CASP-FM (56 proteins) proposed in the SAINT paper, and a recently released PDB structure collection from CAMEO (93 proteins) named as CAMEO93. On these six test sets, OPUS-TASS achieves consistent improvements in both backbone torsion angles prediction and secondary structure prediction. On CAMEO93, SPOT-1D achieves the mean absolute errors of 16.89 and 23.02 for ϕ and ψ predictions, respectively, and the accuracies for 3- and 8-state secondary structure predictions are 87.72 and 77.15%, respectively. In comparison, OPUS-TASS achieves 16.56 and 22.56 for ϕ and ψ predictions, and 89.06 and 78.87% for 3- and 8-state secondary structure predictions, respectively. In particular, after using our torsion angles refinement method OPUS-Refine as the post-processing procedure for OPUS-TASS, the mean absolute errors for final ϕ and ψ predictions are further decreased to 16.28 and 21.98, respectively. Availability and implementation The training and the inference codes of OPUS-TASS and its data are available at https://github.com/thuxugang/opus_tass. Supplementary information Supplementary data are available at Bioinformatics online.
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López de Frutos, Laura, Jorge J. Cebolla, Pilar Irún, Ralf Köhler, and Pilar Giraldo. "Web-Based Bioinformatics Predictors: Recommendations to Assess Lysosomal Cholesterol Trafficking Diseases-Related Genes." Methods of Information in Medicine 58, no. 01 (2019): 050–59. http://dx.doi.org/10.1055/s-0039-1692463.

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Introduction The growing number of genetic variants of unknown significance (VUS) and availability of several in silico prediction tools make the evaluation of potentially deleterious gene variants challenging. Materials and Methods We evaluated several programs and software to determine the one that can predict the impact of genetic variants found in lysosomal storage disorders (LSDs) caused by defects in cholesterol trafficking best. We evaluated the sensitivity, specificity, accuracy, precision, and Matthew's correlation coefficient of the most common software. Results Our findings showed that for exonic variants, only MutPred1 reached 100% accuracy and generated the best predictions (sensitivity and accuracy = 1.00), whereas intronic variants, SROOGLE or Human Splicing Finder (HSF) generated the best predictions (sensitivity = 1.00, and accuracy = 1.00). Discussion Next-generation sequencing substantially increased the number of detected genetic variants, most of which were considered to be VUS, creating a need for accurate pathogenicity prediction. The focus of the present study is the importance of accurately predicting LSDs, with majority of previously unreported specific mutations. Conclusion We found that the best prediction tool for the NPC1, NPC2, and LIPA variants was MutPred1 for exonic regions and HSF and SROOGLE for intronic regions and splice sites.
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Zhang, Tong, Yu Tian, Le Yuan, Fu Chen, Ailin Ren, and Qian-Nan Hu. "Bio2Rxn: sequence-based enzymatic reaction predictions by a consensus strategy." Bioinformatics 36, no. 11 (2020): 3600–3601. http://dx.doi.org/10.1093/bioinformatics/btaa135.

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Abstract Summary The development of sequencing technologies has generated large amounts of protein sequence data. The automated prediction of the enzymatic reactions of uncharacterized proteins is a major challenge in the field of bioinformatics. Here, we present Bio2Rxn as a web-based tool to provide putative enzymatic reaction predictions for uncharacterized protein sequences. Bio2Rxn adopts a consensus strategy by incorporating six types of enzyme prediction tools. It allows for the efficient integration of these computational resources to maximize the accuracy and comprehensiveness of enzymatic reaction predictions, which facilitates the characterization of the functional roles of target proteins in metabolism. Bio2Rxn further links the enzyme function prediction with more than 300 000 enzymatic reactions, which were manually curated by more than 100 people over the past 9 years from more than 580 000 publications. Availability and implementation Bio2Rxn is available at: http://design.rxnfinder.org/bio2rxn/. Contact qnhu@sibs.ac.cn Supplementary information Supplementary data are available at Bioinformatics online.
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Johansson-Åkhe, Isak, Claudio Mirabello, and Björn Wallner. "InterPep2: global peptide–protein docking using interaction surface templates." Bioinformatics 36, no. 8 (2020): 2458–65. http://dx.doi.org/10.1093/bioinformatics/btaa005.

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Abstract Motivation Interactions between proteins and peptides or peptide-like intrinsically disordered regions are involved in many important biological processes, such as gene expression and cell life-cycle regulation. Experimentally determining the structure of such interactions is time-consuming and difficult because of the inherent flexibility of the peptide ligand. Although several prediction-methods exist, most are limited in performance or availability. Results InterPep2 is a freely available method for predicting the structure of peptide–protein interactions. Improved performance is obtained by using templates from both peptide–protein and regular protein–protein interactions, and by a random forest trained to predict the DockQ-score for a given template using sequence and structural features. When tested on 252 bound peptide–protein complexes from structures deposited after the complexes used in the construction of the training and templates sets of InterPep2, InterPep2-Refined correctly positioned 67 peptides within 4.0 Å LRMSD among top10, similar to another state-of-the-art template-based method which positioned 54 peptides correctly. However, InterPep2 displays a superior ability to evaluate the quality of its own predictions. On a previously established set of 27 non-redundant unbound-to-bound peptide–protein complexes, InterPep2 performs on-par with leading methods. The extended InterPep2-Refined protocol managed to correctly model 15 of these complexes within 4.0 Å LRMSD among top10, without using templates from homologs. In addition, combining the template-based predictions from InterPep2 with ab initio predictions from PIPER-FlexPepDock resulted in 22% more near-native predictions compared to the best single method (22 versus 18). Availability and implementation The program is available from: http://wallnerlab.org/InterPep2. Supplementary information Supplementary data are available at Bioinformatics online.
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Weis, Caroline, Max Horn, Bastian Rieck, Aline Cuénod, Adrian Egli, and Karsten Borgwardt. "Topological and kernel-based microbial phenotype prediction from MALDI-TOF mass spectra." Bioinformatics 36, Supplement_1 (2020): i30—i38. http://dx.doi.org/10.1093/bioinformatics/btaa429.

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Abstract Motivation Microbial species identification based on matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) has become a standard tool in clinical microbiology. The resulting MALDI-TOF mass spectra also harbour the potential to deliver prediction results for other phenotypes, such as antibiotic resistance. However, the development of machine learning algorithms specifically tailored to MALDI-TOF MS-based phenotype prediction is still in its infancy. Moreover, current spectral pre-processing typically involves a parameter-heavy chain of operations without analyzing their influence on the prediction results. In addition, classification algorithms lack quantification of uncertainty, which is indispensable for predictions potentially influencing patient treatment. Results We present a novel prediction method for antimicrobial resistance based on MALDI-TOF mass spectra. First, we compare the complex conventional pre-processing to a new approach that exploits topological information and requires only a single parameter, namely the number of peaks of a spectrum to keep. Second, we introduce PIKE, the peak information kernel, a similarity measure specifically tailored to MALDI-TOF mass spectra which, combined with a Gaussian process classifier, provides well-calibrated uncertainty estimates about predictions. We demonstrate the utility of our approach by predicting antibiotic resistance of three clinically highly relevant bacterial species. Our method consistently outperforms competitor approaches, while demonstrating improved performance and security by rejecting out-of-distribution samples, such as bacterial species that are not represented in the training data. Ultimately, our method could contribute to an earlier and precise antimicrobial treatment in clinical patient care. Availability and implementation We make our code publicly available as an easy-to-use Python package under https://github.com/BorgwardtLab/maldi_PIKE.
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Dissertations / Theses on the topic "Bioinformatics predictions"

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Åkesson, Julia. "Robust Community Predictions of Hubs in Gene Regulatory Networks." Thesis, Linköpings universitet, Bioinformatik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-153200.

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Many diseases, such as cardiovascular diseases, cancer and diabetes, originate from several malfunctions in biological systems. The human body is regulated by a wide range of biological systems, composed of biological entities interacting in complex networks, responsible for carrying out specific functions. Some parts of the networks, such as hubs serving as master regulators, are more important for maintaining a function. To find the cause of diseases, where hubs are possible disease regulators, it is critical to know the structure of these biological systems. Such structures can be reverse engineered from high-throughput data with measured levels of biological entities. However, the complexity of biological systems makes inferring their structure a complicated task, demanding the use of computational methods, called network inference methods. Today, many network inference methods have been developed, that predicts the interactions of biological networks, with varying degree of success. In the DREAM5 challenge 35 network inference methods were evaluated on how well interactions in gene regulatory networks (GRNs) were predicted. Herein, in contrast to the DREAM5 challenge, we have evaluated network inference methods’ ability to predict hubs in GRNs. In accordance with the DREAM5 challenge, different methods performed the best on different data sets. Moreover, we discovered that network inference methods were not able to identify hubs from groups of similarly expressed genes. Also, we noticed that hubs in GRNs had a distinct expression in the data, leading to the development of a new method (the PCA method) for the prediction of hubs. Furthermore, the DREAM5 challenge showed that community predictions, combining the predictions from many network inference methods, resulted in more robust predictions of interactions. Herein, the community approach was applied on predicting hubs, with the conclusion that community predictions is the more robust approach. However, we also concluded that it was enough to combine 6-7 network inference methods to achieve robust predictions of hubs.
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Bernsel, Andreas. "Sequence-based predictions of membrane-protein topology, homology and insertion." Doctoral thesis, Stockholms universitet, Institutionen för biokemi och biofysik, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-8126.

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Membrane proteins comprise around 20-30% of a typical proteome and play crucial roles in a wide variety of biochemical pathways. Apart from their general biological significance, membrane proteins are of particular interest to the pharmaceutical industry, being targets for more than half of all available drugs. This thesis focuses on prediction methods for membrane proteins that ultimately rely on their amino acid sequence only. By identifying soluble protein domains in membrane protein sequences, we were able to constrain and improve prediction of membrane protein topology, i.e. what parts of the sequence span the membrane and what parts are located on the cytoplasmic and extra-cytoplasmic sides. Using predicted topology as input to a profile-profile based alignment protocol, we managed to increase sensitivity to detect distant membrane protein homologs. Finally, experimental measurements of the level of membrane integration of systematically designed transmembrane helices in vitro were used to derive a scale of position-specific contributions to helix insertion efficiency for all 20 naturally occurring amino acids. Notably, position within the helix was found to be an important factor for the contribution to helix insertion efficiency for polar and charged amino acids, reflecting the highly anisotropic environment of the membrane. Using the scale to predict natural transmembrane helices in protein sequences revealed that, whereas helices in single-spanning proteins are typically hydrophobic enough to insert by themselves, a large part of the helices in multi-spanning proteins seem to require stabilizing helix-helix interactions for proper membrane integration. Implementing the scale to predict full transmembrane topologies yielded results comparable to the best statistics-based topology prediction methods.
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Yang, Sen. "Disease, Drug, and Target Association Predictions by Integrating Multiple Heterogeneous Sources." Case Western Reserve University School of Graduate Studies / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=case1342194249.

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Wagih, Omar. "Elucidating the mechanistic impact of single nucleotide variants in model organisms." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/271713.

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Understanding how genetic variation propagate to differences in phenotypes in individuals is an ongoing challenge in genetics. Genome-wide association studies have allowed for the identification of many trait-associated genomic loci. However, they are limited in their inability to explain the altered cellular mechanism. Genetic variation can drive disease by altering a range of mechanisms, including signalling networks, TF binding, and protein folding. Understanding the impact of variants on such processes has key implications in therapeutics, drug development, and more. This thesis aims to utilise computational predictors to shed light on how cellular mechanisms are altered in the context of genetic variation and better understand how they drive both molecular and organism-level phenotypes. Many binding events in the cell are mediated by short stretches of sequence motifs. The ability to discover these underlying rules of binding could greatly aid our understanding of variant impact. Kinase–substrate phosphorylation is one of the most prominent post-translational modifications (PTMs) which is mediated by such motifs. We first describe a computational method which utilises interaction and phosphorylation data to predict sequence preferences of kinases. Our method was applied to 57% of human kinases capturing known well-characterised and novel kinase specificities. We experimentally validate four understudied kinases to show that predicted models closely resemble true specificities. We further demonstrate that this method can be applied to different organisms and can be used for other phospho-recognition domains. The described approach allows for an extended repertoire of sequence specificities to be generated, particularly in organisms for which little data is available. TF-DNA binding is another mechanism driven by sequence motifs, which is key for the tight regulation of gene expression and can be greatly altered by genetic variation. We have comprehensively benchmarked current methods used to predict non-coding variant effects on TF-DNA binding by employing over 20,000 compiled allele-specific ChIP-seq variants across 94 TFs. We show that machine learning-based approaches significantly outperform more rudimentary methods such as the position weight matrix. We further note that models for many TFs with distinct binding specificities were unable to accurately assess the impact of variants. For these TFs, we explore alternative mechanisms underlying TF-binding, such as methylation, co-operative binding, and DNA shape that drive poor performance. Our results demonstrate the complexity of predicting non-coding variant effects and the importance of incorporating alternative mechanisms into models. Finally, we describe a comprehensive effort to compile and benchmark state-of-the-art sequence and structure-based predictors of mutational consequences and predict the effect of coding and non-coding variants in the reference genomes of human, yeast, and E. coli. Predicted mechanisms include the impact on protein stability, interaction interfaces, and PTMs. These variant effects are provided through mutfunc, a fast and intuitive web tool by which users can interactively explore pre-computed mechanistic variant impact predictions. We validate computed predictions by analysing known pathogenic disease variants and provide mechanistic hypotheses for causal variants of unknown function. We further use our predictions to devise gene-level functionality scores in human and yeast individuals, which we then used to perform gene-phenotype associations and uncover novel gene-phenotype associations.
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Rezwan, Faisal Ibne. "Improving computational predictions of Cis-regulatory binding sites in genomic data." Thesis, University of Hertfordshire, 2011. http://hdl.handle.net/2299/7133.

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Cis-regulatory elements are the short regions of DNA to which specific regulatory proteins bind and these interactions subsequently influence the level of transcription for associated genes, by inhibiting or enhancing the transcription process. It is known that much of the genetic change underlying morphological evolution takes place in these regions, rather than in the coding regions of genes. Identifying these sites in a genome is a non-trivial problem. Experimental (wet-lab) methods for finding binding sites exist, but all have some limitations regarding their applicability, accuracy, availability or cost. On the other hand computational methods for predicting the position of binding sites are less expensive and faster. Unfortunately, however, these algorithms perform rather poorly, some missing most binding sites and others over-predicting their presence. The aim of this thesis is to develop and improve computational approaches for the prediction of transcription factor binding sites (TFBSs) by integrating the results of computational algorithms and other sources of complementary biological evidence. Previous related work involved the use of machine learning algorithms for integrating predictions of TFBSs, with particular emphasis on the use of the Support Vector Machine (SVM). This thesis has built upon, extended and considerably improved this earlier work. Data from two organisms was used here. Firstly the relatively simple genome of yeast was used. In yeast, the binding sites are fairly well characterised and they are normally located near the genes that they regulate. The techniques used on the yeast genome were also tested on the more complex genome of the mouse. It is known that the regulatory mechanisms of the eukaryotic species, mouse, is considerably more complex and it was therefore interesting to investigate the techniques described here on such an organism. The initial results were however not particularly encouraging: although a small improvement on the base algorithms could be obtained, the predictions were still of low quality. This was the case for both the yeast and mouse genomes. However, when the negatively labeled vectors in the training set were changed, a substantial improvement in performance was observed. The first change was to choose regions in the mouse genome a long way (distal) from a gene over 4000 base pairs away - as regions not containing binding sites. This produced a major improvement in performance. The second change was simply to use randomised training vectors, which contained no meaningful biological information, as the negative class. This gave some improvement over the yeast genome, but had a very substantial benefit for the mouse data, considerably improving on the aforementioned distal negative training data. In fact the resulting classifier was finding over 80% of the binding sites in the test set and moreover 80% of the predictions were correct. The final experiment used an updated version of the yeast dataset, using more state of the art algorithms and more recent TFBSs annotation data. Here it was found that using randomised or distal negative examples once again gave very good results, comparable to the results obtained on the mouse genome. Another source of negative data was tried for this yeast data, namely using vectors taken from intronic regions. Interestingly this gave the best results.
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Ishtiaq, Khandker S. "Robust Modeling and Predictions of Greenhouse Gas Fluxes from Forest and Wetland Ecosystems." FIU Digital Commons, 2015. http://digitalcommons.fiu.edu/etd/2287.

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The land-atmospheric exchanges of carbon dioxide (CO2) and methane (CH4) are major drivers of global warming and climatic changes. The greenhouse gas (GHG) fluxes indicate the dynamics and potential storage of carbon in terrestrial and wetland ecosystems. Appropriate modeling and prediction tools can provide a quantitative understanding and valuable insights into the ecosystem carbon dynamics, while aiding the development of engineering and management strategies to limit emissions of GHGs and enhance carbon sequestration. This dissertation focuses on the development of data-analytics tools and engineering models by employing a range of empirical and semi-mechanistic approaches to robustly predict ecosystem GHG fluxes at variable scales. Scaling-based empirical models were developed by using an extended stochastic harmonic analysis algorithm to achieve spatiotemporally robust predictions of the diurnal cycles of net ecosystem exchange (NEE). A single set of model parameters representing different days/sites successfully estimated the diurnal NEE cycles for various ecosystems. A systematic data-analytics framework was then developed to determine the mechanistic, relative linkages of various climatic and environmental drivers with the GHG fluxes. The analytics, involving big data for diverse ecosystems of the AmeriFLUX network, revealed robust latent patterns: a strong control of radiation-energy variables, a moderate control of temperature-hydrology variables, and a relatively weak control of aerodynamic variables on the terrestrial CO2 fluxes. The data-analytics framework was then employed to determine the relative controls of different climatic, biogeochemical and ecological drivers on CO2 and CH4 fluxes from coastal wetlands. The knowledge was leveraged to develop nonlinear, predictive models of GHG fluxes using a small set of environmental variables. The models were presented in an Excel spreadsheet as an ecological engineering tool to estimate and predict the net ecosystem carbon balance of the wetland ecosystems. The research also investigated the emergent biogeochemical-ecological similitude and scaling laws of wetland GHG fluxes by employing dimensional analysis from fluid mechanics. Two environmental regimes were found to govern the wetland GHG fluxes. The discovered similitude and scaling laws can guide the development of data-based mechanistic models to robustly predict wetland GHG fluxes under a changing climate and environment.
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Papadimitriou, Sofia. "Towards multivariant pathogenicity predictions: Using machine-learning to directly predict and explore disease-causing oligogenic variant combinations." Doctoral thesis, Universite Libre de Bruxelles, 2020. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/312576.

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The emergence of statistical and predictive methods able to analyse genomic data has revolutionised the field of medical genetics, allowing the identification of disease-causing gene variants (i.e. mutations) for several human genetic diseases. Although these approaches have greatly improved our understanding of Mendelian «one gene – one phenotype» genetic models, studying diseases related to more intricate models that involve causative variants in several genes (i.e. oligogenic diseases) still remains a challenge, either due to the lack of sufficient methodologies and disease-specific cohorts to study or the phenotypic complexity associated with such diseases. This situation makes it difficult to not only understand the genetic mechanisms of the disease, but to also offer proper counseling and support to the patient. Until recently, no specialized predictive methods existed to directly predict causative variant combinations for oligogenic diseases. However, with the advent of data on variant combinations in gene pairs (i.e. bilocus variant combinations) leading to disease, collected at the Digenic Diseases Database (DIDA), we hypothesized that the transition from single to variant combination pathogenicity predictors is now possible.To investigate this hypothesis, we organised our research on two main routes. At first, we developed an interpretable variant combination pathogenicity predictor, called VarCoPP, for gene pairs. For this goal, we trained multiple Random Forest algorithms on pathogenic bilocus variant combinations from DIDA against neutral data from the 1000 Genomes Project and investigated the contribution of the incorporated variant, gene and gene pair features to the prediction outcome. In the second part, we explored the usefulness of different gene pair burden scores based on this novel predictive method, in discovering oligogenic signatures in neurodevelopmental diseases, which involve a spectrum of monogenic to polygenic cases. We performed a preliminary analysis on the Deciphering Developmental Diseases (DDD) project containing exome data of 4195 families and assessed the capability of our scores in supporting already diagnosed monogenic cases, discovering significant pairs compared to control cases and linking patients in communities based on the genetic burden they share, using the Leiden community detection algorithm.The performance of VarCoPP shows that it is possible to predict disease-causing bilocus variant combinations with good accuracy both during cross-validation and when testing on new cases. We also show its relevance for disease-related gene panels, and enhanced its clinical applicability by defining confidence zones that guarantee with 95\% or 99\% probability that a prediction is indeed a true positive, guiding clinical researchers towards the most relevant results. This method and additional biological annotations are incorporated in an online platform called ORVAL that allows the prediction and exploration of candidate disease-causing oligogenic variant combinations with predicted gene networks, based on patient variant data. Our preliminary analysis on the DDD cohort shows that - although all bi-locus burden scores show advantages, disadvantages and certain types of biases - taking the maximum pathogenicity score present inside a gene pair seems to provide, at the moment, the most unbiased results. We also show that our predictive methods enable us to detect patient communities inside DDD, based exclusively on the shared pathogenic bi-locus burden between patients, with more than half of these communities containing enriched phenotypic and molecular pathway information. Our predictive method is also able to bring to the surface genes not officially known to be involved in disease, but nevertheless, with a biological relevance, as well as a few examples of potential oligogenicity inside the network, paving the way for further exploration of oligogenic signatures for neurodevelopmental diseases.<br>Doctorat en Sciences<br>info:eu-repo/semantics/nonPublished
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Hennerdal, Aron, and Arne Elofsson. "Rapid membrane protein topology prediction." Stockholms universitet, Institutionen för biokemi och biofysik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-61921.

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State-of-the-art methods for topology of α-helical membrane proteins are based on the use of time-consuming multiple sequence alignments obtained from PSI-BLAST or other sources. Here, we examine if it is possible to use the consensus of topology prediction methods that are based on single sequences to obtain a similar accuracy as the more accurate multiple sequence-based methods. Here, we show that TOPCONS-single performs better than any of the other topology prediction methods tested here, but ~6% worse than the best method that is utilizing multiple sequence alignments. AVAILABILITY AND IMPLEMENTATION: TOPCONS-single is available as a web server from http://single.topcons.net/ and is also included for local installation from the web site. In addition, consensus-based topology predictions for the entire international protein index (IPI) is available from the web server and will be updated at regular intervals.
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Habtemariam, Mesay. "Bioinformatics Approach to Probe Protein-Protein Interactions: Understanding the Role of Interfacial Solvent in the Binding Sites of Protein-Protein Complexes;Network Based Predictions and Analysis of Human Proteins that Play Critical Roles in HIV Pathogenesis." VCU Scholars Compass, 2013. http://scholarscompass.vcu.edu/etd/2997.

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The thesis work contains two projects under the same umbrella. The first project is to provide a detailed analysis on the behavior of interfacial water molecules at protein-protein complexes, in this case focusing on homodimeric complexes, and to investigate their effect with respect to different residue types. For that reason the homodimeric data-set, which includes high-resolution (≤ 2.30 Å) X-ray crystal structures of 252 (140 Biological & 112 Non-biological) protein complexes was chosen to explore fundamental differences between interfaces that Nature has “engineered” vs. compared to interfaces found under man-made conditions. The data set was comprised of 5391 water molecules where a maximum of 4 Å from both interfacing proteins. Our analysis is applied a suite of modeling tools based on HINT, a program for hydropathic analysis developed in our laboratory. HINT is based on the experimental measurement of the hydrophobic effect. The second project is designed to explore various means of suppressing the expression of human genes that play critical role in HIV pathogenesis. To achieve this aim, a data set of Affymetrix Human HG Focus Target Array, which measures the expression levels of HIV seronegative and seropositive individuals in human PBMCs, was analyzed with Pathway Studio 9.0 software. This work gives insight into the elucidation of the important mechanisms of human proteins interactions in HIV seropositive individuals and their implications. Hence, we found the kind and types of microRNAs that are suppressing the human genes which have great role for HIV replication in a cell.
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Hvidsten, Torgeir R. "Predicting Function of Genes and Proteins from Sequence, Structure and Expression Data." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis : Univ.-bibl. [distributör], 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-4490.

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Books on the topic "Bioinformatics predictions"

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Majoros, William H. Methods for computational gene prediction. Cambridge University Press, 2007.

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Methods for computational gene prediction. Cambridge University Press, 2007.

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Rangwala, Huzefa. Introduction to protein structure prediction: Methods and algorithms. Wiley, 2010.

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Trevor, Hastie, Tibshirani Robert, and SpringerLink (Online service), eds. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer-Verlag New York, 2009.

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1950-, Tsigelny Igor F., ed. Protein structure prediction: Bioinformatic approach. International University Line, 2002.

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Majoros, William H. Methods for Computational Gene Prediction. Cambridge University Press, 2007.

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Majoros, William H. Methods for Computational Gene Prediction. Cambridge University Press, 2007.

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Kihara, Daisuke. Protein Function Prediction for Omics Era. Springer, 2011.

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Protein Function Prediction for Omics Era. Springer, 2011.

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Karypis, George, and Huzefa Rangwala. Introduction to Protein Structure Prediction: Methods and Algorithms. Wiley & Sons, Incorporated, John, 2010.

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Book chapters on the topic "Bioinformatics predictions"

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Rastogi, Shruti, and Burkhard Rost. "Bioinformatics Predictions of Localization and Targeting." In Methods in Molecular Biology. Humana Press, 2010. http://dx.doi.org/10.1007/978-1-60327-412-8_17.

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Urda, Daniel, Francisco J. Veredas, Ignacio Turias, and Leonardo Franco. "Addition of Pathway-Based Information to Improve Predictions in Transcriptomics." In Bioinformatics and Biomedical Engineering. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17935-9_19.

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Azé, Jérôme, Thomas Bourquard, Sylvie Hamel, Anne Poupon, and David W. Ritchie. "Using Kendall-τ Meta-Bagging to Improve Protein-Protein Docking Predictions." In Pattern Recognition in Bioinformatics. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24855-9_25.

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Watson, James D., Roman A. Laskowski, and Janet M. Thornton. "Case Studies: Function Predictions of Structural Genomics Results." In From Protein Structure to Function with Bioinformatics. Springer Netherlands, 2017. http://dx.doi.org/10.1007/978-94-024-1069-3_14.

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Rajaraman, Ashok, Cedric Chauve, and Yann Ponty. "Assessing the Robustness of Parsimonious Predictions for Gene Neighborhoods from Reconciled Phylogenies: Supplementary Material." In Bioinformatics Research and Applications. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19048-8_22.

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Huang, Jim C., and Brendan J. Frey. "STORMSeq: A Method for Ranking Regulatory Sequences by Integrating Experimental Datasets with Diverse Computational Predictions." In Handbook of Statistical Bioinformatics. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-16345-6_6.

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Notaro, Marco, Max Schubach, Marco Frasca, Marco Mesiti, Peter N. Robinson, and Giorgio Valentini. "Ensembling Descendant Term Classifiers to Improve Gene - Abnormal Phenotype Predictions." In Computational Intelligence Methods for Bioinformatics and Biostatistics. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14160-8_8.

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Mrozek, Dariusz. "Cloud Services for Efficient Ab Initio Predictions of 3D Protein Structures." In Scalable Big Data Analytics for Protein Bioinformatics. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98839-9_5.

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Biswas, Abhishek, Desh Ranjan, Mohammad Zubair, and Jing He. "A Novel Computational Method for Deriving Protein Secondary Structure Topologies Using Cryo-EM Density Maps and Multiple Secondary Structure Predictions." In Bioinformatics Research and Applications. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19048-8_6.

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Di Lena, Pietro, Luciano Margara, Marco Vassura, Piero Fariselli, and Rita Casadio. "A New Protein Representation Based on Fragment Contacts: Towards an Improvement of Contact Maps Predictions." In Computational Intelligence Methods for Bioinformatics and Biostatistics. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02504-4_19.

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Conference papers on the topic "Bioinformatics predictions"

1

Wald, Randall, Taghi M. Khoshgoftaar, Richard Zuech, and Amri Napolitano. "Network Traffic Prediction Models for Near- and Long-Term Predictions." In 2014 IEEE International Conference on Bioinformatics and Bioengineering (BIBE). IEEE, 2014. http://dx.doi.org/10.1109/bibe.2014.69.

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Lambrou, Antonis, Harris Papadopoulos, and Alexander Gammerman. "Calibrated probabilistic predictions for biomedical applications." In 2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE). IEEE, 2012. http://dx.doi.org/10.1109/bibe.2012.6399676.

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Pastorino, Javier, and Ashis Kumer Biswas. "TexAnASD: Text Analytics for ASD Risk Gene Predictions." In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2019. http://dx.doi.org/10.1109/bibm47256.2019.8983107.

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Azzopardi, Joseph, and Jean Ebejer. "LigityScore: Convolutional Neural Network for Binding-affinity Predictions." In 12th International Conference on Bioinformatics Models, Methods and Algorithms. SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010228300380049.

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Li, Ruowang, Jiayi Tong, Rui Duan, Yong Chen, and Jason Moore. "Evaluation of Phenotyping Errors on Polygenic Risk Score Predictions." In 11th International Conference on Bioinformatics Models, Methods and Algorithms. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0008935301230130.

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ALIOTO, TYLER S., RODERIC GUIGÓ, ERNESTO PICARDI, and GRAZIANO PESOLE. "GenePC AND ASPIC INTEGRATE GENE PREDICTIONS WITH EXPRESSED SEQUENCE ALIGNMENTS TO PREDICT ALTERNATIVE TRANSCRIPTS." In The 6th Asia-Pacific Bioinformatics Conference. PUBLISHED BY IMPERIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO., 2007. http://dx.doi.org/10.1142/9781848161092_0037.

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Lim, Hong Seo, Madeline E. Smerchansky, Jingxuan Zhou, et al. "Image-based early predictions of functional properties in cell manufacturing." In 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2020. http://dx.doi.org/10.1109/bibm49941.2020.9313297.

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Li, Jiang, Ayyappa Vadlamudi, Shao-Hui Chuang, et al. "Combining Prostate Cancer Region Predictions from MALDI Spectra Processing and Texture Analysis." In 2010 IEEE International Conference on BioInformatics and BioEngineering. IEEE, 2010. http://dx.doi.org/10.1109/bibe.2010.20.

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Das, Dibyajyoti, Sowmya Ramaswamy Krishnan, Gopalakrishnan Bulusu, and Arijit Roy. "Computational predictions of host-pathogen interactions using domain and sequence signature." In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2019. http://dx.doi.org/10.1109/bibm47256.2019.8983364.

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Pantazis, Yannis, Christos Tselas, Kleanthi Lakiotaki, Vincenzo Lagani, and Ioannis Tsamardinos. "Latent Feature Representations for Human Gene Expression Data Improve Phenotypic Predictions." In 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2020. http://dx.doi.org/10.1109/bibm49941.2020.9313286.

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