Using prior knowledge and rule induction methods to discover molecular markers of prognosis in lung cancer.

Lewis Frey, Mary Elizabeth Edgerton, Douglas H. Fisher, Lianhong Tang, Zhihua Chen

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

An iterative computational scientific discovery approach is proposed and applied to gene expression data for resectable lung adenocarcinoma patients. We use genes learned from the C5.0 rule induction algorithm, clinical features and prior knowledge derived from a network of interacting genes as represented in a database obtained with PathwayAssist to discover markers for prognosis in the gene expression data. This is done in an iterative fashion with machine learning techniques seeding the prior knowledge. This research illustrates the utility of combining signaling networks and machine learning techniques to produce simple prognostic classifiers.

Original languageEnglish (US)
Pages (from-to)256-260
Number of pages5
JournalAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
StatePublished - Jan 1 2005

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Lung Neoplasms
Gene Expression
Gene Regulatory Networks
Databases
Research
Genes
Machine Learning
Adenocarcinoma of lung

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Using prior knowledge and rule induction methods to discover molecular markers of prognosis in lung cancer. / Frey, Lewis; Edgerton, Mary Elizabeth; Fisher, Douglas H.; Tang, Lianhong; Chen, Zhihua.

In: AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium, 01.01.2005, p. 256-260.

Research output: Contribution to journalArticle

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