Finding correlated bicluster from gene expression data of Alzheimer disease using FABIA biclustering method

Nuning Setyaningrum, Alhadi Bustamam, Titin Siswantining

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)


Alzheimer's is a disease chronic neuro-degenerative progressive disorder in the adult human brain that tends to be old and which causes memory, thought and behavior problems and the most common causes of dementia that usually worsen over time. The microarray data of AD gene expression were taken from six different brain regions size 54675 id probes genes x 161 samples in 74 normal samples and 87 samples affected by Alzheimer's. The development of microarray technology used in dataset genetic expression includes Alzheimer's. To discover hidden pattern from microarray of Alzheimer's gene expression data we propose finding correlated bicluster using FABIA biclustering. In this paper, we apply the analysis for bicluster acquisition (FABIA) factor technique for gene expression matrix data that has been standardized median centering and normalized, and then this multiplicative method produces two very sparse Laplacian variables with heavy-tailed non-Gaussian signals for the desired bicluster. To measure information on the bicluster we use I / NI. Evaluate the results of our cycle; we use the Jaccard index and the Munkres algorithm which implemented in the Truecluster in the open source R package. From the experimental results, there are nine biclusters formed by data's AD that can be analyzed.

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
Publication statusPublished - 22 Mar 2019
EventInternational Symposium on BioMathematics 2018, SYMOMATH 2018 - Depok, Indonesia
Duration: 31 Aug 20182 Sept 2018

Publication series

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


ConferenceInternational Symposium on BioMathematics 2018, SYMOMATH 2018


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