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Tkachev V, Sorokin M, Mescheryakov A, Simonov A, Garazha A, Buzdin A, Muchnik I, Borisov N. FLOating-Window Projective Separator (FloWPS): A Data Trimming Tool for Support Vector Machines (SVM) to Improve Robustness of the Classifier. Front Genet 2019; 9:717. [PMID: 30697229 PMCID: PMC6341065 DOI: 10.3389/fgene.2018.00717] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2018] [Accepted: 12/21/2018] [Indexed: 01/31/2023] Open
Abstract
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.
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Affiliation(s)
- Victor Tkachev
- Department of Bioinformatics and Molecular Networks, OmicsWay Corporation, Walnut, CA, United States
| | - Maxim Sorokin
- Department of Bioinformatics and Molecular Networks, OmicsWay Corporation, Walnut, CA, United States.,Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia
| | | | - Alexander Simonov
- Department of Bioinformatics and Molecular Networks, OmicsWay Corporation, Walnut, CA, United States
| | - Andrew Garazha
- Department of Bioinformatics and Molecular Networks, OmicsWay Corporation, Walnut, CA, United States
| | - Anton Buzdin
- Department of Bioinformatics and Molecular Networks, OmicsWay Corporation, Walnut, CA, United States.,Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia.,I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Ilya Muchnik
- Hill Center, Rutgers University, Piscataway, NJ, United States
| | - Nicolas Borisov
- Department of Bioinformatics and Molecular Networks, OmicsWay Corporation, Walnut, CA, United States.,I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
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Zhavoronkov A, Mamoshina P, Vanhaelen Q, Scheibye-Knudsen M, Moskalev A, Aliper A. Artificial intelligence for aging and longevity research: Recent advances and perspectives. Ageing Res Rev 2019; 49:49-66. [PMID: 30472217 DOI: 10.1016/j.arr.2018.11.003] [Citation(s) in RCA: 84] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2018] [Revised: 11/07/2018] [Accepted: 11/21/2018] [Indexed: 12/14/2022]
Abstract
The applications of modern artificial intelligence (AI) algorithms within the field of aging research offer tremendous opportunities. Aging is an almost universal unifying feature possessed by all living organisms, tissues, and cells. Modern deep learning techniques used to develop age predictors offer new possibilities for formerly incompatible dynamic and static data types. AI biomarkers of aging enable a holistic view of biological processes and allow for novel methods for building causal models-extracting the most important features and identifying biological targets and mechanisms. Recent developments in generative adversarial networks (GANs) and reinforcement learning (RL) permit the generation of diverse synthetic molecular and patient data, identification of novel biological targets, and generation of novel molecular compounds with desired properties and geroprotectors. These novel techniques can be combined into a unified, seamless end-to-end biomarker development, target identification, drug discovery and real world evidence pipeline that may help accelerate and improve pharmaceutical research and development practices. Modern AI is therefore expected to contribute to the credibility and prominence of longevity biotechnology in the healthcare and pharmaceutical industry, and to the convergence of countless areas of research.
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