Abstract
Modelling Transcriptional Regulation with a Mixture of Factor Analyzers and Variational Bayesian Expectation Maximization
Highlights
Transcriptional gene regulation is a complex process that utilizes a network of interactions
We have investigated the application of Bayesian mixtures of factor analyzers (MFA-Variational Bayesian Expectation Maximization (VBEM)) to modelling transcriptional regulation in cells
Like recent approaches based on Bayesian factor analysis applied to the same problem [16, 17], MFAVBEM allows for the fact that transcription factors (TFs) are often subject to post-translational modifications and that their true activities are usually unknown
Summary
Transcriptional gene regulation is a complex process that utilizes a network of interactions. This process is primarily controlled by diverse regulatory proteins called transcription factors (TFs), which bind to specific DNA sequences and thereby repress or initiate gene expression. Transcriptional regulatory networks control the expression levels of thousands of genes as part of diverse biological processes such as the cell cycle, embryogenesis, host-pathogen interactions, and circadian rhythms. To keep the notation simple, we use the same letter p(·) for every probability distribution, even though they might be of different functional forms. The form of p(·) will become clear from its argument, with p(x) and p(y) denoting different distributions (strictly speaking, this should be written as px(x) and py(y)). We do distinguish between scalars and vectors/matrices, using bold-face letters for the latter, and using the superscript “ ” to denote transposition
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