Emerging Translational Bioinformatics: Knowledge-Guided Biomarker Identification for Cancer Diagnostics

• Type of Material: Article
• Authors: John Phan, Emory University; Qiqin Yin-Goen, Emory University; Andrew Young, Emory University; Dongmei Wang, Emory University
• Language: English
• Date: 2009-01-01
• Publisher: IEEE
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2009, IEEE
• Final Published Version (URL): http://dx.doi.org/10.1109/IEMBS.2009.5333937
• Title of Journal or Parent Work: 2009 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-20
• Volume: 2009
• Start Page: 4162
• End Page: +
• Keywords: Engineering, Biomedical; CROSS-VALIDATION; Engineering; MICROARRAY; Science & Technology; CLASSIFICATION; Technology
• Research Categories: Biology, Bioinformatics; Health Sciences, Oncology

Abstract

Advances in high-throughput genomic and proteomic technology have led to a growing interest in cancer biomarkers. These biomarkers can potentially improve the accuracy of cancer subtype prediction and subsequently, the success of therapy. In this paper, we describe emerging technology for enabling translational bioinformatics by improving biomarker identification. Specifically, we present an application that uses prior knowledge to identify the most biologically relevant gene ranking algorithm. Identification of statistically and biologically relevant biomarkers from high-throughput data can be unreliable due to the nature of the data--e.g., high technical variability, small sample size, and high dimension size. Furthermore, due to the lack of available training samples, data-driven machine learning methods are often insufficient without the support of knowledge-based algorithms. As a case study, we apply these knowledge-driven methods to renal cancer data and identify genes that are potential biomarkers for cancer subtype classification.