Analyzing TCGA Lung Cancer Genomic and Expression Data Using SVM With Embedded Parameter Tuning
H. Zhao; A. Deeter; Z.-H. Duan Integrated Bioscience Program, Department of Computer Science, The University of Akron, Akron, OH, United States
Abstract
High-throughput, next-generation sequencing revolutionized genomic sequencing techniques and changed biomedical research in a profound way. The big genomic data generated from next-generation sequencing presents unique scientific challenges and opportunities. One of these challenges is to understand and characterize the patterns of genomic mutation and gene expression in cancer. In this study, we explored these patterns in lung cancer using support vector machines (SVMs) with embedded ...
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