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Статті в журналах з теми "Omic network inference"

Автор: Grafiati

Опубліковано: 20 квітня 2024

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1

Nagpal, Sunil, Rashmi Singh, Deepak Yadav, and Sharmila S. Mande. "MetagenoNets: comprehensive inference and meta-insights for microbial correlation networks." Nucleic Acids Research 48, W1 (2020): W572—W579. http://dx.doi.org/10.1093/nar/gkaa254.

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Abstract Microbial association networks are frequently used for understanding and comparing community dynamics from microbiome datasets. Inferring microbial correlations for such networks and obtaining meaningful biological insights, however, requires a lengthy data management workflow, choice of appropriate methods, statistical computations, followed by a different pipeline for suitably visualizing, reporting and comparing the associations. The complexity is further increased with the added dimension of multi-group ‘meta-data’ and ‘inter-omic’ functional profiles that are often associated wit

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2

Dohlman, Anders B., and Xiling Shen. "Mapping the microbial interactome: Statistical and experimental approaches for microbiome network inference." Experimental Biology and Medicine 244, no. 6 (2019): 445–58. http://dx.doi.org/10.1177/1535370219836771.

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Advances in high-throughput sequencing have ushered in a new era of research into the gut microbiome and its role in human health and disease. However, due to the unique characteristics of microbiome survey data, their use for the detection of ecological interaction networks remains a considerable challenge, and a field of active methodological development. In this review, we discuss the landscape of existing statistical and experimental methods for detecting and characterizing microbial interactions, as well as the role that host and environmental metabolic signals play in mediating the behav

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3

Ramos, Susana Isabel, Zarmeen Mussa, Bruno Giotti, Alexander Tsankov, and Nadejda Tsankova. "EPCO-25. MULTI-OMIC ANALYSIS OF THE GLIOBLASTOMA EPIGENOME AND TRANSCRIPTOME INFORMS OF MIGRATORY INTERNEURON-LIKE DEVELOPMENTAL REGULATORS." Neuro-Oncology 24, Supplement_7 (2022): vii121. http://dx.doi.org/10.1093/neuonc/noac209.460.

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Abstract Recent studies have demonstrated that, despite their nomenclature, gliomas recapitulate an interneuron progenitor-like state that drives tumor progression. During human neurodevelopment, interneurons arise from the subcortical ganglionic eminences and migrate tangentially into the neocortex, settling in the cortical plate where they integrate local neurocircuitry. Analogously, malignant glioblastoma (GBM) cells migrate from the tumor core into the surrounding healthy tissue. This innate infiltrative property renders these malignant cells elusive to surgical resection, leading to tumor

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4

Grund, Eric M., A. James Moser, Corinne L. DeCicco, et al. "Abstract 5145: Project Survival®: Discovery of a molecular-clinical phenome biomarker panel to detect pancreatic ductal adenocarcinoma among at risk populations using high-fidelity longitudinal phenotypic and multi-omic analysis." Cancer Research 82, no. 12_Supplement (2022): 5145. http://dx.doi.org/10.1158/1538-7445.am2022-5145.

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Abstract Delayed diagnosis and rapid progression are major drivers of poor survival outcomes for pancreatic ductal adenocarcinoma (PDAC). PDAC is expected to be the second leading cause of cancer death and has a dismal 5 year survival rate of 10%. There is an urgent unmet need to detect the disease at an early stage and stratify patients into more effective treatment regimens within clinically meaningful timeframes. To accomplish this, robust quality controlled OMIC molecular profiling platforms and analytic solutions need to be deployed into precision medicine protocols to discover actionable

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5

Nathasingh, Brandon, Derek Walkama, Laurel Mayhew, et al. "Abstract LB181: Infer cancer cell gene dependency in multiple myeloma using causal AI in-silico patient model." Cancer Research 83, no. 8_Supplement (2023): LB181. http://dx.doi.org/10.1158/1538-7445.am2023-lb181.

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Abstract Recent advances in artificial intelligence (AI) and availability of multimodal patient datasets have enabled the construction of complex network models to derive disease molecular mechanisms and predict the impact of therapeutic intervention. However, observational datasets are commonly affected by confounding factors making causal interpretation challenging. Causal inference network methods are particularly suited to facilitate therapeutic intervention studies by inferring the causal structure from sufficiently detailed multi-omic molecular data. The learned models enable in-silico l

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6

Ye, Qing, and Nancy Lan Guo. "Inferencing Bulk Tumor and Single-Cell Multi-Omics Regulatory Networks for Discovery of Biomarkers and Therapeutic Targets." Cells 12, no. 1 (2022): 101. http://dx.doi.org/10.3390/cells12010101.

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There are insufficient accurate biomarkers and effective therapeutic targets in current cancer treatment. Multi-omics regulatory networks in patient bulk tumors and single cells can shed light on molecular disease mechanisms. Integration of multi-omics data with large-scale patient electronic medical records (EMRs) can lead to the discovery of biomarkers and therapeutic targets. In this review, multi-omics data harmonization methods were introduced, and common approaches to molecular network inference were summarized. Our Prediction Logic Boolean Implication Networks (PLBINs) have advantages o

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7

Alanis-Lobato, Gregorio, Thomas E. Bartlett, Qiulin Huang, et al. "MICA: a multi-omics method to predict gene regulatory networks in early human embryos." Life Science Alliance 7, no. 1 (2023): e202302415. http://dx.doi.org/10.26508/lsa.202302415.

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Recent advances in single-cell omics have transformed characterisation of cell types in challenging-to-study biological contexts. In contexts with limited single-cell samples, such as the early human embryo inference of transcription factor-gene regulatory network (GRN) interactions is especially difficult. Here, we assessed application of different linear or non-linear GRN predictions to single-cell simulated and human embryo transcriptome datasets. We also compared how expression normalisation impacts on GRN predictions, finding that transcripts per million reads outperformed alternative met

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8

Wang, Pei. "Network biology: Recent advances and challenges." Gene & Protein in Disease 1, no. 2 (2022): 101. http://dx.doi.org/10.36922/gpd.v1i2.101.

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Biological networks have garnered widespread attention. The development of biological networks has spawned the birth of a new interdisciplinary field – network biology. Network biology involves the exploration of complex biological systems through biological networks for better understanding of biological functions. This paper reviews some of the recent development of network biology. On the one hand, various approaches to constructing different types of biological networks are reviewed, and the pros and cons of each approach are discussed; on the other hand, the recent advances of information

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9

Yan, Yan, Feng Jiang, Xinan Zhang, and Tianhai Tian. "Inference of Molecular Regulatory Systems Using Statistical Path-Consistency Algorithm." Entropy 24, no. 5 (2022): 693. http://dx.doi.org/10.3390/e24050693.

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One of the key challenges in systems biology and molecular sciences is how to infer regulatory relationships between genes and proteins using high-throughout omics datasets. Although a wide range of methods have been designed to reverse engineer the regulatory networks, recent studies show that the inferred network may depend on the variable order in the dataset. In this work, we develop a new algorithm, called the statistical path-consistency algorithm (SPCA), to solve the problem of the dependence of variable order. This method generates a number of different variable orders using random sam

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10

Bonnet, Eric, Laurence Calzone, and Tom Michoel. "Integrative Multi-omics Module Network Inference with Lemon-Tree." PLOS Computational Biology 11, no. 2 (2015): e1003983. http://dx.doi.org/10.1371/journal.pcbi.1003983.

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11

Wang, Huange, Joao Paulo, Willem Kruijer, et al. "Genotype–phenotype modeling considering intermediate level of biological variation: a case study involving sensory traits, metabolites and QTLs in ripe tomatoes." Molecular BioSystems 11, no. 11 (2015): 3101–10. http://dx.doi.org/10.1039/c5mb00477b.

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12

Zarayeneh, Neda, Euiseong Ko, Jung Hun Oh, et al. "Integration of multi-omics data for integrative gene regulatory network inference." International Journal of Data Mining and Bioinformatics 18, no. 3 (2017): 223. http://dx.doi.org/10.1504/ijdmb.2017.087178.

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13

Kang, Mingon, Donghyun Kim, Jean Gao, et al. "Integration of multi-omics data for integrative gene regulatory network inference." International Journal of Data Mining and Bioinformatics 18, no. 3 (2017): 223. http://dx.doi.org/10.1504/ijdmb.2017.10008266.

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14

Hu, Xinlin, Yaohua Hu, Fanjie Wu, Ricky Wai Tak Leung, and Jing Qin. "Integration of single-cell multi-omics for gene regulatory network inference." Computational and Structural Biotechnology Journal 18 (2020): 1925–38. http://dx.doi.org/10.1016/j.csbj.2020.06.033.

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15

Peñagaricano, F. "S0101 Causal inference of molecular networks integrating multi-omics data." Journal of Animal Science 94, suppl_4 (2016): 2. http://dx.doi.org/10.2527/jas2016.94supplement42a.

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16

Peñagaricano, F. "0412 Causal inference of molecular networks integrating multi-omics data." Journal of Animal Science 94, suppl_5 (2016): 199–200. http://dx.doi.org/10.2527/jam2016-0412.

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17

Sun, Xiaoqiang, Ji Zhang, and Qing Nie. "Inferring latent temporal progression and regulatory networks from cross-sectional transcriptomic data of cancer samples." PLOS Computational Biology 17, no. 3 (2021): e1008379. http://dx.doi.org/10.1371/journal.pcbi.1008379.

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Unraveling molecular regulatory networks underlying disease progression is critically important for understanding disease mechanisms and identifying drug targets. The existing methods for inferring gene regulatory networks (GRNs) rely mainly on time-course gene expression data. However, most available omics data from cross-sectional studies of cancer patients often lack sufficient temporal information, leading to a key challenge for GRN inference. Through quantifying the latent progression using random walks-based manifold distance, we propose a latent-temporal progression-based Bayesian metho

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18

Gao, Wenliang, Wei Kong, Shuaiqun Wang, Gen Wen, and Yaling Yu. "Biomarker Genes Discovery of Alzheimer’s Disease by Multi-Omics-Based Gene Regulatory Network Construction of Microglia." Brain Sciences 12, no. 9 (2022): 1196. http://dx.doi.org/10.3390/brainsci12091196.

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Microglia, the major immune cells in the brain, mediate neuroinflammation, increased oxidative stress, and impaired neurotransmission in Alzheimer’s disease (AD), in which most AD risk genes are highly expressed. In microglia, due to the limitations of current single-omics data analysis, risk genes, the regulatory mechanisms, the mechanisms of action of immune responses and the exploration of drug targets for AD immunotherapy are still unclear. Therefore, we proposed a method to integrate multi-omics data based on the construction of gene regulatory networks (GRN), by combining weighted gene c

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19

Federico, Anthony, Joseph Kern, Xaralabos Varelas, and Stefano Monti. "Structure Learning for Gene Regulatory Networks." PLOS Computational Biology 19, no. 5 (2023): e1011118. http://dx.doi.org/10.1371/journal.pcbi.1011118.

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Inference of biological network structures is often performed on high-dimensional data, yet is hindered by the limited sample size of high throughput “omics” data typically available. To overcome this challenge, often referred to as the “small n, large p problem,” we exploit known organizing principles of biological networks that are sparse, modular, and likely share a large portion of their underlying architecture. We present SHINE—Structure Learning for Hierarchical Networks—a framework for defining data-driven structural constraints and incorporating a shared learning paradigm for efficient

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20

Cha, Junha, and Insuk Lee. "Single-cell network biology for resolving cellular heterogeneity in human diseases." Experimental & Molecular Medicine 52, no. 11 (2020): 1798–808. http://dx.doi.org/10.1038/s12276-020-00528-0.

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AbstractUnderstanding cellular heterogeneity is the holy grail of biology and medicine. Cells harboring identical genomes show a wide variety of behaviors in multicellular organisms. Genetic circuits underlying cell-type identities will facilitate the understanding of the regulatory programs for differentiation and maintenance of distinct cellular states. Such a cell-type-specific gene network can be inferred from coregulatory patterns across individual cells. Conventional methods of transcriptome profiling using tissue samples provide only average signals of diverse cell types. Therefore, rec

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21

Capobianco, Enrico. "Next Generation Networks: Featuring the Potential Role of Emerging Applications in Translational Oncology." Journal of Clinical Medicine 8, no. 5 (2019): 664. http://dx.doi.org/10.3390/jcm8050664.

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Nowadays, networks are pervasively used as examples of models suitable to mathematically represent and visualize the complexity of systems associated with many diseases, including cancer. In the cancer context, the concept of network entropy has guided many studies focused on comparing equilibrium to disequilibrium (i.e., perturbed) conditions. Since these conditions reflect both structural and dynamic properties of network interaction maps, the derived topological characterizations offer precious support to conduct cancer inference. Recent innovative directions have emerged in network medicin

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22

Han, Xudong, Bing Wang, Chenghao Situ, et al. "scapGNN: A graph neural network–based framework for active pathway and gene module inference from single-cell multi-omics data." PLOS Biology 21, no. 11 (2023): e3002369. http://dx.doi.org/10.1371/journal.pbio.3002369.

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Although advances in single-cell technologies have enabled the characterization of multiple omics profiles in individual cells, extracting functional and mechanistic insights from such information remains a major challenge. Here, we present scapGNN, a graph neural network (GNN)-based framework that creatively transforms sparse single-cell profile data into the stable gene–cell association network for inferring single-cell pathway activity scores and identifying cell phenotype–associated gene modules from single-cell multi-omics data. Systematic benchmarking demonstrated that scapGNN was more a

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23

Kim, So Yeon, Eun Kyung Choe, Manu Shivakumar, Dokyoon Kim, and Kyung-Ah Sohn. "Multi-layered network-based pathway activity inference using directed random walks: application to predicting clinical outcomes in urologic cancer." Bioinformatics 37, no. 16 (2021): 2405–13. http://dx.doi.org/10.1093/bioinformatics/btab086.

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Abstract Motivation To better understand the molecular features of cancers, a comprehensive analysis using multi-omics data has been conducted. In addition, a pathway activity inference method has been developed to facilitate the integrative effects of multiple genes. In this respect, we have recently proposed a novel integrative pathway activity inference approach, iDRW and demonstrated the effectiveness of the method with respect to dichotomizing two survival groups. However, there were several limitations, such as a lack of generality. In this study, we designed a directed gene–gene graph u

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24

Vincent, Jonathan, Pierre Martre, Benjamin Gouriou, et al. "RulNet: A Web-Oriented Platform for Regulatory Network Inference, Application to Wheat –Omics Data." PLOS ONE 10, no. 5 (2015): e0127127. http://dx.doi.org/10.1371/journal.pone.0127127.

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25

Schneider, Nimisha, Sergey Korkhov, Alexis Foroozan, Scott Marshall, and Renee Deehan. "Causal inferencing of -omics data from The Cancer Genome Atlas: Lung adenocarcinoma tumors for mechanistic disease characterization and feature engineering." Journal of Clinical Oncology 38, no. 15_suppl (2020): e21016-e21016. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e21016.

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e21016 Background: Advances in high throughput measurement technologies (-omics data) have made it possible to generate high complexity, high volume data for oncology research. Researchers are often confronted many more measurements than samples (p > > > n), which poses challenges for both modeling the complexity of disease at the molecular mechanism level, and overfitting when generating predictive models with complex data. Here, we applied a prior knowledge-driven approach to characterize and classify heavy versus light smokers with lung cancer from The Cancer Genome Atlas, an open

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26

Yuan, Lin, Le-Hang Guo, Chang-An Yuan, et al. "Integration of Multi-Omics Data for Gene Regulatory Network Inference and Application to Breast Cancer." IEEE/ACM Transactions on Computational Biology and Bioinformatics 16, no. 3 (2019): 782–91. http://dx.doi.org/10.1109/tcbb.2018.2866836.

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27

Panchal, Viral, and Daniel F. Linder. "Reverse engineering gene networks using global–local shrinkage rules." Interface Focus 10, no. 1 (2019): 20190049. http://dx.doi.org/10.1098/rsfs.2019.0049.

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Inferring gene regulatory networks from high-throughput ‘omics’ data has proven to be a computationally demanding task of critical importance. Frequently, the classical methods break down owing to the curse of dimensionality, and popular strategies to overcome this are typically based on regularized versions of the classical methods. However, these approaches rely on loss functions that may not be robust and usually do not allow for the incorporation of prior information in a straightforward way. Fully Bayesian methods are equipped to handle both of these shortcomings quite naturally, and they

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28

Chen, Chen, Enakshi Saha, Dawn L. DeMeo, John Quackenbush, and Camila M. Lopes-Ramos. "Abstract 3490: Unveiling sex differences in lung adenocarcinoma through multi-omics integrative protein signaling networks." Cancer Research 84, no. 6_Supplement (2024): 3490. http://dx.doi.org/10.1158/1538-7445.am2024-3490.

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Abstract Sex differences in lung adenocarcinoma (LUAD) are evident in incidence rates, prognostic outcomes, and therapy responses, yet the underlying molecular mechanisms driving these disparities remain underexplored. In this study, we conducted a comprehensive proteogenomic analysis encompassing 38 females and 73 males with LUAD from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset. Employing Transcription Inference using Gene Expression and Regulatory data (TIGER), we inferred sex-differentially activated transcription factors (TFs) from The Cancer Genome Atlas (TCGA) LUAD g

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29

Wani, Nisar, and Khalid Raza. "MKL-GRNI: A parallel multiple kernel learning approach for supervised inference of large-scale gene regulatory networks." PeerJ Computer Science 7 (January 28, 2021): e363. http://dx.doi.org/10.7717/peerj-cs.363.

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High throughput multi-omics data generation coupled with heterogeneous genomic data fusion are defining new ways to build computational inference models. These models are scalable and can support very large genome sizes with the added advantage of exploiting additional biological knowledge from the integration framework. However, the limitation with such an arrangement is the huge computational cost involved when learning from very large datasets in a sequential execution environment. To overcome this issue, we present a multiple kernel learning (MKL) based gene regulatory network (GRN) infere

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30

Qian, Yichun, and Shao-shan Carol Huang. "Improving plant gene regulatory network inference by integrative analysis of multi-omics and high resolution data sets." Current Opinion in Systems Biology 22 (August 2020): 8–15. http://dx.doi.org/10.1016/j.coisb.2020.07.010.

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31

Benedetti, Elisa, Nathalie Gerstner, Maja Pučić-Baković, et al. "Systematic Evaluation of Normalization Methods for Glycomics Data Based on Performance of Network Inference." Metabolites 10, no. 7 (2020): 271. http://dx.doi.org/10.3390/metabo10070271.

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Glycomics measurements, like all other high-throughput technologies, are subject to technical variation due to fluctuations in the experimental conditions. The removal of this non-biological signal from the data is referred to as normalization. Contrary to other omics data types, a systematic evaluation of normalization options for glycomics data has not been published so far. In this paper, we assess the quality of different normalization strategies for glycomics data with an innovative approach. It has been shown previously that Gaussian Graphical Models (GGMs) inferred from glycomics data a

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32

Conard, Ashley Mae, Nathaniel Goodman, Yanhui Hu, et al. "TIMEOR: a web-based tool to uncover temporal regulatory mechanisms from multi-omics data." Nucleic Acids Research 49, W1 (2021): W641—W653. http://dx.doi.org/10.1093/nar/gkab384.

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Abstract Uncovering how transcription factors regulate their targets at DNA, RNA and protein levels over time is critical to define gene regulatory networks (GRNs) and assign mechanisms in normal and diseased states. RNA-seq is a standard method measuring gene regulation using an established set of analysis stages. However, none of the currently available pipeline methods for interpreting ordered genomic data (in time or space) use time-series models to assign cause and effect relationships within GRNs, are adaptive to diverse experimental designs, or enable user interpretation through a web-b

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33

Zeng, Irene Sui Lan, and Thomas Lumley. "Review of Statistical Learning Methods in Integrated Omics Studies (An Integrated Information Science)." Bioinformatics and Biology Insights 12 (January 1, 2018): 117793221875929. http://dx.doi.org/10.1177/1177932218759292.

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Integrated omics is becoming a new channel for investigating the complex molecular system in modern biological science and sets a foundation for systematic learning for precision medicine. The statistical/machine learning methods that have emerged in the past decade for integrated omics are not only innovative but also multidisciplinary with integrated knowledge in biology, medicine, statistics, machine learning, and artificial intelligence. Here, we review the nontrivial classes of learning methods from the statistical aspects and streamline these learning methods within the statistical learn

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34

Neutsch, Steffen, Caroline Heneka, and Marcus Brüggen. "Inferring astrophysics and dark matter properties from 21 cm tomography using deep learning." Monthly Notices of the Royal Astronomical Society 511, no. 3 (2022): 3446–62. http://dx.doi.org/10.1093/mnras/stac218.

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ABSTRACT 21 cm tomography opens a window to directly study astrophysics and fundamental physics of early epochs in our Universe’s history, the Epoch of Reionization (EoR) and Cosmic Dawn (CD). Summary statistics such as the power spectrum omit information encoded in this signal due to its highly non-Gaussian nature. Here, we adopt a network-based approach for direct inference of CD and EoR astrophysics jointly with fundamental physics from 21 cm tomography. We showcase a warm dark matter (WDM) universe, where dark matter density parameter Ωm and WDM mass mWDM strongly influence both CD and EoR

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35

Ultsch, Alfred, and Jörn Lötsch. "Robust Classification Using Posterior Probability Threshold Computation Followed by Voronoi Cell Based Class Assignment Circumventing Pitfalls of Bayesian Analysis of Biomedical Data." International Journal of Molecular Sciences 23, no. 22 (2022): 14081. http://dx.doi.org/10.3390/ijms232214081.

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Bayesian inference is ubiquitous in science and widely used in biomedical research such as cell sorting or “omics” approaches, as well as in machine learning (ML), artificial neural networks, and “big data” applications. However, the calculation is not robust in regions of low evidence. In cases where one group has a lower mean but a higher variance than another group, new cases with larger values are implausibly assigned to the group with typically smaller values. An approach for a robust extension of Bayesian inference is proposed that proceeds in two main steps starting from the Bayesian po

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36

Yang, Jiyuan, Sheetal Bhatara, Masayuki Umeda, et al. "Dissecting Subtype-Specific Tumor-Time Interactions and Underlying Hidden Drivers in Pediatric Acute Myeloid Leukemia Via Single-Cell Multi-Omics." Blood 142, Supplement 1 (2023): 5977. http://dx.doi.org/10.1182/blood-2023-189178.

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Introduction: Pediatric acute myeloid leukemia (AML) is a heterogeneous hematological malignancy characterized by various chromosomal abnormalities and somatic mutations. Investigating the origin of tumorigenesis and deciphering the complex interplay between AML cells and their surrounding tumor immune microenvironment (TIME) is crucial to understand the underlying mechanisms driving disease progression and response to therapy. Recent advancements in single-cell multi-omics technologies enabled us to characterize the transcriptional (GEX) and epigenetic (ATAC) landscapes of both tumors and TIM

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Fang, Yan, Jiayin Yu, Yumei Ding, and Xiaohua Lin. "Inferring Complementary and Substitutable Products Based on Knowledge Graph Reasoning." Mathematics 11, no. 22 (2023): 4709. http://dx.doi.org/10.3390/math11224709.

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Complementarity and substitutability between products are essential concepts in retail and marketing. To achieve this, existing approaches take advantage of knowledge graphs to learn more evidence for inference. However, they often omit the knowledge that lies in the unstructured data. In this research, we concentrate on inferring complementary and substitutable products in e-commerce from mass structured and unstructured data. An improved knowledge-graph-based reasoning model has been proposed which cannot only derive related products but also provide interpretable paths to explain the relati

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38

Klepikova, Anna V., and Aleksey A. Penin. "Gene Expression Maps in Plants: Current State and Prospects." Plants 8, no. 9 (2019): 309. http://dx.doi.org/10.3390/plants8090309.

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For many years, progress in the identification of gene functions has been based on classical genetic approaches. However, considerable recent omics developments have brought to the fore indirect but high-resolution methods of gene function identification such as transcriptomics, proteomics, and metabolomics. A transcriptome map is a powerful source of functional information and the result of the genome-wide expression analysis of a broad sampling of tissues and/or organs from different developmental stages and/or environmental conditions. In plant science, the application of transcriptome maps

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39

Chen, Xi, Yuan Wang, Antonio Cappuccio, et al. "Mapping disease regulatory circuits at cell-type resolution from single-cell multiomics data." Nature Computational Science 3, no. 7 (2023): 644–57. http://dx.doi.org/10.1038/s43588-023-00476-5.

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AbstractResolving chromatin-remodeling-linked gene expression changes at cell-type resolution is important for understanding disease states. Here we describe MAGICAL (Multiome Accessibility Gene Integration Calling and Looping), a hierarchical Bayesian approach that leverages paired single-cell RNA sequencing and single-cell transposase-accessible chromatin sequencing from different conditions to map disease-associated transcription factors, chromatin sites, and genes as regulatory circuits. By simultaneously modeling signal variation across cells and conditions in both omics data types, MAGIC

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40

Guo, Tingbo, Haiqi Zhu, Xiao Wang, et al. "Abstract 2072: Computational modeling of metabolic variations in tumor microenvironment." Cancer Research 83, no. 7_Supplement (2023): 2072. http://dx.doi.org/10.1158/1538-7445.am2023-2072.

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Abstract Dysregulation of metabolic pathways is a hallmark of cancer. Despite a plethora of knowledge on the core components of metabolic pathways we have gained, there are still major gaps in our understanding of the integrated behavior and metabolic heterogeneity of cells in the context of their microenvironment. Essentially, metabolic behavior can be determined by different factors and vary dramatically from cell to cell due to their high plasticity, driven by the need to cope with various dynamic metabolic requirements. Large amount of single-cell, spatial or tissue multi-omics data obtain

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41

Jin, Qiao, and Ronald Ching Wan Ma. "Metabolomics in Diabetes and Diabetic Complications: Insights from Epidemiological Studies." Cells 10, no. 11 (2021): 2832. http://dx.doi.org/10.3390/cells10112832.

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The increasing prevalence of diabetes and its complications, such as cardiovascular and kidney disease, remains a huge burden globally. Identification of biomarkers for the screening, diagnosis, and prognosis of diabetes and its complications and better understanding of the molecular pathways involved in the development and progression of diabetes can facilitate individualized prevention and treatment. With the advancement of analytical techniques, metabolomics can identify and quantify multiple biomarkers simultaneously in a high-throughput manner. Providing information on underlying metaboli

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42

Schwaber, Jessica L., Darren Korbie, Stacey Andersen, et al. "Network mapping of primary CD34+ cells by Ampliseq based whole transcriptome targeted resequencing identifies unexplored differentiation regulatory relationships." PLOS ONE 16, no. 2 (2021): e0246107. http://dx.doi.org/10.1371/journal.pone.0246107.

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With the exception of a few master transcription factors, regulators of neutrophil maturation are poorly annotated in the intermediate phenotypes between the granulocyte-macrophage progenitor (GMP) and the mature neutrophil phenotype. Additional challenges in identifying gene expression regulators in differentiation pathways relate to challenges wherein starting cell populations are heterogeneous in lineage potential and development, are spread across various states of quiescence, as well as sample quality and input limitations. These factors contribute to data variability make it difficult to

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43

Majumdar, Abhishek, Yueze Liu, Yaoqin Lu, Shaofeng Wu, and Lijun Cheng. "kESVR: An Ensemble Model for Drug Response Prediction in Precision Medicine Using Cancer Cell Lines Gene Expression." Genes 12, no. 6 (2021): 844. http://dx.doi.org/10.3390/genes12060844.

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Background: Cancer cell lines are frequently used in research as in-vitro tumor models. Genomic data and large-scale drug screening have accelerated the right drug selection for cancer patients. Accuracy in drug response prediction is crucial for success. Due to data-type diversity and big data volume, few methods can integrative and efficiently find the principal low-dimensional manifold of the high-dimensional cancer multi-omics data to predict drug response in precision medicine. Method: A novelty k-means Ensemble Support Vector Regression (kESVR) is developed to predict each drug response

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44

Clark, Natalie M., Trevor M. Nolan, Ping Wang, et al. "Integrated omics networks reveal the temporal signaling events of brassinosteroid response in Arabidopsis." Nature Communications 12, no. 1 (2021). http://dx.doi.org/10.1038/s41467-021-26165-3.

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AbstractBrassinosteroids (BRs) are plant steroid hormones that regulate cell division and stress response. Here we use a systems biology approach to integrate multi-omic datasets and unravel the molecular signaling events of BR response in Arabidopsis. We profile the levels of 26,669 transcripts, 9,533 protein groups, and 26,617 phosphorylation sites from Arabidopsis seedlings treated with brassinolide (BL) for six different lengths of time. We then construct a network inference pipeline called Spatiotemporal Clustering and Inference of Omics Networks (SC-ION) to integrate these data. We use o

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45

Ben Guebila, Marouen, Tian Wang, Camila M. Lopes-Ramos, et al. "The Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks." Genome Biology 24, no. 1 (2023). http://dx.doi.org/10.1186/s13059-023-02877-1.

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AbstractInference and analysis of gene regulatory networks (GRNs) require software that integrates multi-omic data from various sources. The Network Zoo (netZoo; netzoo.github.io) is a collection of open-source methods to infer GRNs, conduct differential network analyses, estimate community structure, and explore the transitions between biological states. The netZoo builds on our ongoing development of network methods, harmonizing the implementations in various computing languages and between methods to allow better integration of these tools into analytical pipelines. We demonstrate the utili

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46

Kim, Daniel, Andy Tran, Hani Jieun Kim, Yingxin Lin, Jean Yee Hwa Yang, and Pengyi Yang. "Gene regulatory network reconstruction: harnessing the power of single-cell multi-omic data." npj Systems Biology and Applications 9, no. 1 (2023). http://dx.doi.org/10.1038/s41540-023-00312-6.

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AbstractInferring gene regulatory networks (GRNs) is a fundamental challenge in biology that aims to unravel the complex relationships between genes and their regulators. Deciphering these networks plays a critical role in understanding the underlying regulatory crosstalk that drives many cellular processes and diseases. Recent advances in sequencing technology have led to the development of state-of-the-art GRN inference methods that exploit matched single-cell multi-omic data. By employing diverse mathematical and statistical methodologies, these methods aim to reconstruct more comprehensive

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47

Fotuhi Siahpirani, Alireza, Sara Knaack, Deborah Chasman, et al. "Dynamic regulatory module networks for inference of cell type-specific transcriptional networks." Genome Research, June 15, 2022, gr.276542.121. http://dx.doi.org/10.1101/gr.276542.121.

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Changes in transcriptional regulatory networks can significantly alter cell fate. To gain insight into transcriptional dynamics, several studies have profiled bulk multi-omic datasets with parallel transcriptomic and epigenomic measurements at different stages of a developmental process. However, integrating these data to infer cell type-specific regulatory networks is a major challenge. We present Dynamic Regulatory Module Networks (DRMNs), a novel approach to infer cell type-specific cis-regulatory networks and their dynamics. DRMN integrates expression, chromatin state and accessibility to

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48

Ogris, Christoph, Yue Hu, Janine Arloth, and Nikola S. Müller. "Versatile knowledge guided network inference method for prioritizing key regulatory factors in multi-omics data." Scientific Reports 11, no. 1 (2021). http://dx.doi.org/10.1038/s41598-021-85544-4.

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AbstractConstantly decreasing costs of high-throughput profiling on many molecular levels generate vast amounts of multi-omics data. Studying one biomedical question on two or more omic levels provides deeper insights into underlying molecular processes or disease pathophysiology. For the majority of multi-omics data projects, the data analysis is performed level-wise, followed by a combined interpretation of results. Hence the full potential of integrated data analysis is not leveraged yet, presumably due to the complexity of the data and the lacking toolsets. We propose a versatile approach,

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49

Capobianco, Enrico, Elisabetta Marras, and Antonella Travaglione. "Multiscale Characterization of Signaling Network Dynamics through Features." Statistical Applications in Genetics and Molecular Biology 10, no. 1 (2011). http://dx.doi.org/10.2202/1544-6115.1657.

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Inference methods applied to biological networks suffer from a main criticism: as the latter reflect associations measured under static conditions, an evaluation of the underlying modular organization can be biologically meaningful only if the dynamics can also be taken into consideration. The same limitation is present in protein interactome networks. Given the substantial uncertainty characterizing protein interactions, we identify at least three aspects that must be considered for inference purposes: 1. Coverage, which for most organisms is only partial; 2. Stochasticity, affecting both the

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50

Zhang, Shilu, Saptarshi Pyne, Stefan Pietrzak, et al. "Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets." Nature Communications 14, no. 1 (2023). http://dx.doi.org/10.1038/s41467-023-38637-9.

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AbstractCell type-specific gene expression patterns are outputs of transcriptional gene regulatory networks (GRNs) that connect transcription factors and signaling proteins to target genes. Single-cell technologies such as single cell RNA-sequencing (scRNA-seq) and single cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq), can examine cell-type specific gene regulation at unprecedented detail. However, current approaches to infer cell type-specific GRNs are limited in their ability to integrate scRNA-seq and scATAC-seq measurements and to model network dynamics on a

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