MCNF : A Novel Method for Cancer Subtyping by Integrating Multi-Omics and Clinical Data

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

8 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)1682-1690
Journal / PublicationIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume17
Issue number5
Online published11 Apr 2019
Publication statusPublished - Sept 2020

Abstract

In the age of personalized medicine, there is a great need to classify cancer (from the same organ site) into homogeneous subtypes. Recent technology advancements in genome-wide molecular profiling have made it possible to profiling multiple molecular datasets to characterize the genomic changes in various cancer types. How to take full advantage of the availability of these omics data? And how to integrate these molecular data with patient clinical data to do a more systematic subtyping of cancer are the focuses of the paper. We proposed a new method called Molecular and Clinical Networks Fusion (MCNF) to classify cancer into homogeneous subtypes. Our method has two highlights: one is that it can integrate both numerical and non-numerical data into the fused network; the next highlight is that it is unsupervised, which means it can automatically determine the optimal number of clusters.

Research Area(s)

  • Cancer, Radio frequency, DNA, Bioinformatics, Gene expression, Genomics, Cancer heterogeneity, multi-omics data, network integration, unsupervised classification, HETEROGENEITY, DISCOVERY, BREAST, JOINT, MODEL