Semantic Scholar Open Access 2018 1422 sitasi

Applications of Support Vector Machine (SVM) Learning in Cancer Genomics.

Shujun Huang Nianguang Cai Pedro Penzuti Pacheco Shavira Narrandes Yang Wang +1 lainnya

Lihat Sumber DOI

Abstrak

Machine learning with maximization (support) of separating margin (vector), called support vector machine (SVM) learning, is a powerful classification tool that has been used for cancer genomic classification or subtyping. Today, as advancements in high-throughput technologies lead to production of large amounts of genomic and epigenomic data, the classification feature of SVMs is expanding its use in cancer genomics, leading to the discovery of new biomarkers, new drug targets, and a better understanding of cancer driver genes. Herein we reviewed the recent progress of SVMs in cancer genomic studies. We intend to comprehend the strength of the SVM learning and its future perspective in cancer genomic applications.

Topik & Kata Kunci

Computer Science Medicine

Penulis (6)

Shujun Huang

Nianguang Cai

Pedro Penzuti Pacheco

Shavira Narrandes

Yang Wang

Wayne W. Xu

Format Sitasi

APA MLA BibTeX

Huang, S., Cai, N., Pacheco, P.P., Narrandes, S., Wang, Y., Xu, W.W. (2018). Applications of Support Vector Machine (SVM) Learning in Cancer Genomics.. https://doi.org/10.21873/CGP.20063

Akses Cepat

Lihat di Sumber doi.org/10.21873/CGP.20063

Informasi Jurnal

Tahun Terbit: 2018
Bahasa: en
Total Sitasi: 1422×
Sumber Database: Semantic Scholar
DOI: 10.21873/CGP.20063
Akses: Open Access ✓