Discriminant and Network Analysis to Study Origin of Cancer

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

*此作品的通信作者

研究成果: 圖書/報告稿件的類型章節同行評審

摘要

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.

原文英語
主出版物標題Statistical Diagnostics for Cancer
主出版物子標題Analyzing High-Dimensional Data
發行者Wiley-VCH
頁面193-214
頁數22
3
ISBN(列印)9783527332625
DOIs
出版狀態已出版 - 08 04 2013
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