Multi-classification and Variable Selection Techniques in Cancer Genomic Data Research |Biomedgrid

In the past two decades, a huge amount of high-throughput -omics data, such as genomics, transcriptomics, metabolomics, and proteomics, have been generated regarding variations in DNA, RNA, or protein features for many cancers. The tremendous volume and complexity of these data bring significant challenges for biostatisticians, biologists, and clinicians. One of the central goal of analyzing these data is disease classification, which is fundamental for us to explore knowledge, formulate diagnosis, and develop personalized treatment. Here, we review the statistical and machine learning techniques studied in cancer classification and the process or difficulties of categorizing cancer subtypes from their genomic features.