Emerging translational bioinformatics: knowledge-guided biomarker identification for cancer diagnostics.
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.
Phan
John H
JH
Department of biomedical engineering at Georgia Tech and Emory University, Atlanta, GA, USA. jhphan@gatech.edu
Yin-Goen
Qiqin
Q
Young
Andrew N
AN
Wang
May D
MD
D000465:Algorithms; D019540:Area Under Curve; D001185:Artificial Intelligence; D015415:Biomarkers / Q000737:chemistry*; D014408:Biomarkers, Tumor / Q000737:chemistry*; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods; D015972:Gene Expression Regulation, Neoplastic; D023281:Genomics; D006801:Humans; D008495:Medical Oncology / Q000295:instrumentation / Q000379:methods*; D015233:Models, Statistical; D009369:Neoplasms / Q000175:diagnosis* / Q000378:metabolism*; D020411:Oligonucleotide Array Sequence Analysis; D020133:Reverse Transcriptase Polymerase Chain Reaction
D015415:Biomarkers; D014408:Biomarkers, Tumor