Wani, N. & Raza, K. (2019). Integrative approaches to reconstruct regulatory networks from multi-omics data: A review of state-of-the-art methods. Computational Biology and Chemistry, Elsevier, 83: 107120 https://doi.org/10.1016/j.compbiolchem.2019.107120
Wani, N. & Raza, K. (2019). Integrative approaches to reconstruct regulatory networks from multi-omics data: A review of state-of-the-art methods. Computational Biology and Chemistry, Elsevier, 83: 107120 https://doi.org/10.1016/j.compbiolchem.2019.107120
Wani, N. & Raza, K. (2019). Integrative approaches to reconstruct regulatory networks from multi-omics data: A review of state-of-the-art methods. Computational Biology and Chemistry, Elsevier, 83: 107120 https://doi.org/10.1016/j.compbiolchem.2019.107120
Wani, N. & Raza, K. (2019). Integrative approaches to reconstruct regulatory networks from multi-omics data: A review of state-of-the-art methods. Computational Biology and Chemistry, Elsevier, 83: 107120 https://doi.org/10.1016/j.compbiolchem.2019.107120
Abstract
Data generation using high throughput technologies has led to the accumulation of diverse types of molecular data.These data have different types (discrete,real,string etc.) and occur in various formats and sizes. Datasets including gene expression, miRNA expression, protein-DNA binding data (ChIP-Seq/ChIP-ChIP), mutation data(copy number variation, single nucleotide polymorphisms), GO annotations, protein-protein interaction and disease-gene association data are some of the commonly used genomic datasets to study biological processes. Each of them provides a unique, complementary and partly independent view of the genome and hence embed essential information about their regulatory mechanisms. In order to understand the functions of genes, proteins and analyze mechanisms arising out of their interactions, information provided by each of these datasets individually may not be sufficient. Therefore integrating these multi-omic data and inferring regulatory interactions from the integrated dataset provides a system level biological insights in predicting gene functions and their phenotypic outcomes. To study genome functionality through interaction networks, different methods have been proposed for collective mining of information from an integrated dataset. We survey here data integration approaches using state-of-the-art techniques such as network integration, Bayesian networks, regularized regression (LASSO) and multiple kernel learning methods.
Keywords
network inference; data integration; regulatory networks; transcription factor; gene expression
Subject
Computer Science and Mathematics, Mathematical and Computational Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
