Reverse engineering directed gene regulatory networks from transcriptomics and proteomics data of biomining bacterial communities with approximate Bayesian computation and steady-state signalling simulations

Reverse engineering directed gene regulatory networks from transcriptomics and proteomics data of biomining bacterial communities with approximate Bayesian computation and steady-state signalling simulations

Abstract Background Network inference is an important aim of systems biology. It enables the transformation of OMICs datasets into biological knowledge. It consists of reverse engineering gene regulatory networks from OMICs data, such as RNAseq or mass spectrometry-based proteomics data, through com...

詳細記述

書誌詳細
主要な著者: Antoine Buetti-Dinh, Malte Herold, Stephan Christel, Mohamed El Hajjami, Francesco Delogu, Olga Ilie, Sören Bellenberg, Paul Wilmes, Ansgar Poetsch, Wolfgang Sand, Mario Vera, Igor V. Pivkin, Ran Friedman, Mark Dopson
フォーマット: 論文
言語:English
出版事項: BMC 2020-01-01
シリーズ:BMC Bioinformatics
主題:
Biological signalling simulations
Gene regulatory networks
Approximate Bayesian computation
Machine learning
Biomining
Acidophiles
オンライン･アクセス:https://doi.org/10.1186/s12859-019-3337-9

インターネット

https://doi.org/10.1186/s12859-019-3337-9

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