Correction: Inferring Protein Modulation from Gene Expression Data Using Conditional Mutual Information
| Autor: | Jung Hoon Woo, Brygida Bisikirska, Andrea Califano, Gonzalo Lopez, Mukesh Bansal, Federico M. Giorgi |
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| Přispěvatelé: | Giorgi, Federico M, Lopez, Gonzalo, Woo, Jung H, Bisikirska, Brygida, Califano, Andrea, Bansal, Mukesh |
| Rok vydání: | 2016 |
| Předmět: |
Computer science
Gene Expression lcsh:Medicine computer.software_genre Bioinformatics Models Biological Text mining Modulation (music) Humans Gene Regulatory Networks Bioinformatics Networks Cancer Conditional Mutual Information RNA Messenger lcsh:Science Oligonucleotide Array Sequence Analysis Multidisciplinary Sequence Analysis RNA business.industry Gene Expression Profiling Systems Biology Conditional mutual information lcsh:R Correction Proteins Bayes Theorem Gene Expression Regulation Section (archaeology) lcsh:Q Data mining business computer Algorithms |
| Zdroj: | PLoS ONE, Vol 11, Iss 9, p e0163402 (2016) PLoS ONE |
| ISSN: | 1932-6203 |
| Popis: | Systematic, high-throughput dissection of causal post-translational regulatory dependencies, on a genome wide basis, is still one of the great challenges of biology. Due to its complexity, however, only a handful of computational algorithms have been developed for this task. Here we present CINDy (Conditional Inference of Network Dynamics), a novel algorithm for the genome-wide, context specific inference of regulatory dependencies between signaling protein and transcription factor activity, from gene expression data. The algorithm uses a novel adaptive partitioning methodology to accurately estimate the full Condition Mutual Information (CMI) between a transcription factor and its targets, given the expression of a signaling protein. We show that CMI analysis is optimally suited to dissecting post-translational dependencies. Indeed, when tested against a gold standard dataset of experimentally validated protein-protein interactions in signal transduction networks, CINDy significantly outperforms previous methods, both in terms of sensitivity and precision. |
| Databáze: | OpenAIRE |
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