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

By Shujun Huang, Nianguang Cai, Pedro Penzuti Pacheco, Shavira Narrandes, Wayne W. Xu. Cancer Genomics & Proteomics, 2018

1,478 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/24e6c5bfe9bb0751e5708b501d04e860011b2953

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In Molecular Biology

1. Christy M Babu
2. Swetha Dhamercherla
3. M. Ahsan
4. Suresh Dara
5. S. S. Jadav

Citations in the corpus

• Machine Learning in Drug Discovery: A Review (2021, methodology): “The SVM approach (Huang et al. 2018) was used to quantify anti-cancer drugs based on cancer cell properties.”
• A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation (2023, background): “SVMs have been prominently used in a number of challenging applications such as handwriting recognition [222], and cancer genomics[223].”
• Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine (2020, methodology): “Some of its reported contributions include diagnosing mental illness (106), detecting knee osteoarthritis (159), monitoring medical wireless sensors (160), diagnosing Alzheimer’s disease (161), predicting metabolic pathways (162), predicting outcomes of a patient’s encounter with behavioral health p”
• What Is Machine Learning, Artificial Neural Networks and Deep Learning?—Examples of Practical Applications in Medicine (2023, background): “It excels at recognising intricate patterns in complex datasets and finds applications across diverse areas, like handwriting recognition, credit card fraud detection, and facial recognition [24].”