Discriminant and Network Analysis to Study Origin of Cancer

Li Chen, Ye Tian, Guoqiang Yu, David J. Miller, Ie Ming Shih, Yue Wang

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Enabled by rapid advances in biological data acquisition technologies and developments in computational methodologies, interdisciplinary research in machine learning for biomedicine tackles various challenging biological questions by comprehensively scrutinizing (multiplatform) data from multiple, distinct vantages. Understanding the origin and progression of cancer has great practical import for advancing both biological knowledge and potential clinical treatments. Technically, the most challenging biological questions inspire and promote the development and applications of novel computational methods. This chapter presents a coalition of state-of-the-art machine learning methods and leading-edge scientific puzzles. With DNA copy number and transcriptome data, we were able to design specific statistical hypothesis tests to reveal the origin of cancer by comparing the genomic and transcriptome codes and biological network structures.

Original languageEnglish (US)
Title of host publicationStatistical Diagnostics for Cancer
Subtitle of host publicationAnalyzing High-Dimensional Data
PublisherWiley-VCH
Pages193-214
Number of pages22
Volume3
ISBN (Print)9783527332625
DOIs
StatePublished - Apr 8 2013

Keywords

  • Comparative genomic hybridization (CGH)
  • Copy number alteration (CNA)
  • Differential dependency network (DDN)
  • Fallopian tube (FT)
  • Ovarian cancer
  • Ovarian surface epithelium (OSE)

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)

Fingerprint Dive into the research topics of 'Discriminant and Network Analysis to Study Origin of Cancer'. Together they form a unique fingerprint.

Cite this