Research Article Open Access
Volume 2 | Issue 1 | DOI: https://doi.org/10.33696/Proteomics.2.008

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

  • 1Statistics and Epidemiology Unit, Warwick Medical School, University of Warwick, Coventry, UK
  • 2Warwick Cancer Research Centre, University of Warwick, Coventry, UK
  • 3The Alan Turing Institute for Data Science and Artificial Intelligence, The British Library, London, UK
+ Affiliations - Affiliations

Corresponding Author

Deepak Parashar, D.Parashar@warwick.ac.uk

Received Date: July 02, 2021

Accepted Date: July 27, 2021


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 immensely useful in data-centric engineering and recently researchers have also sought its applications in healthcare. Despite their wide applicability, SVMs are not yet in the mainstream of toolkits to be utilised in observational clinical studies or in clinical trials. This research investigates the very role of SVMs in stratifying the patient population based on a continuous biomarker across a variety of datasets. Based on the mathematical framework underlying SVMs, we formulate and fit algorithms in the context of biomarker stratified cancer datasets to evaluate their merits. The analysis reveals their superior performance for certain data-types when compared to other ML methods suggesting that SVMs may have the potential to provide a robust yet simplistic solution to stratify real cancer patients based on continuous biomarkers, and hence accelerate the identification of subgroups for improved clinical outcomes or guide targeted cancer therapies.

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