FLOating-Window Projective Separator (FloWPS): A Data Trimming Tool for Support Vector Machines (SVM) to Improve Robustness of the Classifier

Frontiers in Genetics

Alexander Simonov¹  Victor Tkachev¹  Andrew Garazha¹  Ilya Muchnik²  Nicolas Borisov³  Anton Buzdin³  Maxim Sorokin⁴  Artem Mescheryakov⁵ 

[1] Department of Bioinformatics and Molecular Networks, OmicsWay Corporation, Walnut, CA, United States;Hill Center, Rutgers University, Piscataway, NJ, United States;I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia;Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia;Yandex N.V. Corporation, Moscow, Russia;

关键词: bioinformatics;    machine learning;    oncology;    gene expression;    support vector machines;    personalized medicine;   

DOI  :  10.3389/fgene.2018.00717

来源: DOAJ

【 摘 要 】

Here, we propose a heuristic technique of data trimming for SVM termed FLOating Window Projective Separator (FloWPS), tailored for personalized predictions based on molecular data. This procedure can operate with high throughput genetic datasets like gene expression or mutation profiles. Its application prevents SVM from extrapolation by excluding non-informative features. FloWPS requires training on the data for the individuals with known clinical outcomes to create a clinically relevant classifier. The genetic profiles linked with the outcomes are broken as usual into the training and validation datasets. The unique property of FloWPS is that irrelevant features in validation dataset that don’t have significant number of neighboring hits in the training dataset are removed from further analyses. Next, similarly to the k nearest neighbors (kNN) method, for each point of a validation dataset, FloWPS takes into account only the proximal points of the training dataset. Thus, for every point of a validation dataset, the training dataset is adjusted to form a floating window. FloWPS performance was tested on ten gene expression datasets for 992 cancer patients either responding or not on the different types of chemotherapy. We experimentally confirmed by leave-one-out cross-validation that FloWPS enables to significantly increase quality of a classifier built based on the classical SVM in most of the applications, particularly for polynomial kernels.

【 授权许可】

Unknown