Flexible model-based joint probabilistic clustering of binary and continuous inputs and its application to genetic regulation and cancer

Zainul Abidin, Fatin Nurzahirah Binti (2017) Flexible model-based joint probabilistic clustering of binary and continuous inputs and its application to genetic regulation and cancer. PhD thesis, University of Leeds.

Abstract

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Keywords: clustering, mixture-model, next-generation sequencing, multi-omics, transcriptomics, genomics, ChIP-seq, RNA-seq, mutations, gene-expression, transcriptional regulatory networks, survival-analysis, cancer, AML, yeast
Awarding institution: University of Leeds
Academic Units: The University of Leeds > Faculty of Biological Sciences (Leeds) > Institute for Molecular and Cellular Biology (Leeds)
Identification Number/EthosID (e.g. uk.bl.ethos.123456): uk.bl.ethos.729461
Depositing User: Miss Fatin Nurzahirah Zainul Abidin
Date Deposited: 05 Dec 2017 11:58
Last Modified: 25 Jul 2018 09:56

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Supplementary Material

Filename: FlexiCoClusteringPackage-master.zip

Description: FlexiCoClustering software

Licence: Creative Commons GNU GPL (Software)

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