Do Support Vector Machines Play a Role in Stratifying Patient Population Based on Cancer Biomarkers?

ABSTRACT: Biomarkers are known to be the key driver behind targeted cancer therapies by either stratifying the patients into risk categories or identifying patient subgroups most likely to benefit. However, the ability of a biomarker to stratify patients relies heavily on the type of clinical endpoint data being collected. Of particular interest is the scenario when the biomarker involved is a continuous one where the challenge is often to identify cut-offs or thresholds that would stratify the population according to the level of clinical outcome or treatment benefit. On the other hand, there are well-established Machine Learning (ML) methods such as the Support Vector Machines (SVM) that classify data, both linear as well as non-linear, into subgroups in an optimal way. SVMs have proven to be imme

SUBMITTER: Lanza B 

PROVIDER: S-EPMC7611982 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature