Differential gene co-expression network using BicMix

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1 Citation (Scopus)

Abstract

Alzheimer's is a dangerous disease that causes dementia. Malfunctioned gene in the brain caused by Alzheimer Disease (AD) make some problem in the brain (e.g memory). Recovering network of the gene in the AD from Alzheimer's gene expression data is essential to understand the information about AD. In this research, we want to find groups of genes that co-expressed in some condition, called biclusters, and find the network of those genes based on that group. The problem to find the accurate network/information is the unknown external factor that affects the measurement. Here we use probability-based biclustering to cover the uncertainty. We use BicMix, a new probabilistic-based biclustering method to find biclusters of gene and the gene expression network. This method use a Bayesian framework and models the data as a result of the multiplication of two sparse matrices. The value of these matrices represents whether or not a gene or a condition included in a bicluster. Three-Parameter Beta (TPB) distribution and variational expectation maximization (VEM) is respectively used to induce the sparsity of these matrices and to estimate the parameters. Once we get the biclusters, the result can be used to build the gene co-expression network.

Original languageEnglish
Title of host publicationProceedings of the Symposium on BioMathematics, SYMOMATH 2018
EditorsBevina Desjwiandra Handari, Hiromi Seno, Hengki Tasman
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735418141
DOIs
Publication statusPublished - 22 Mar 2019
EventInternational Symposium on BioMathematics 2018, SYMOMATH 2018 - Depok, Indonesia
Duration: 31 Aug 20182 Sep 2018

Publication series

NameAIP Conference Proceedings
Volume2084
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

ConferenceInternational Symposium on BioMathematics 2018, SYMOMATH 2018
CountryIndonesia
CityDepok
Period31/08/182/09/18

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