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Article


Title

DBFE: distribution-based feature extraction from structural variants in whole-genome data

Authors

[ 1 ] Instytut Informatyki, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ P ] employee

Scientific discipline (Law 2.0)

[2.3] Information and communication technology

Year of publication

2022

Published in

Bioinformatics

Journal year: 2022 | Journal volume: vol. 38 | Journal number: iss. 19

Article type

scientific article

Publication language

english

Abstract

EN Motivation: Whole-genome sequencing has revolutionized biosciences by providing tools for constructing complete DNA sequences of individuals. With entire genomes at hand, scientists can pinpoint DNA fragments responsible for oncogenesis and predict patient responses to cancer treatments. Machine learning plays a paramount role in this process. However, the sheer volume of whole-genome data makes it difficult to encode the characteristics of genomic variants as features for learning algorithms. Results: In this article, we propose three feature extraction methods that facilitate classifier learning from sets of genomic variants. The core contributions of this work include: (i) strategies for determining features using variant length binning, clustering and density estimation; (ii) a programing library for automating distribution-based feature extraction in machine learning pipelines. The proposed methods have been validated on five real-world datasets using four different classification algorithms and a clustering approach. Experiments on genomes of 219 ovarian, 61 lung and 929 breast cancer patients show that the proposed approaches automatically identify genomic biomarkers associated with cancer subtypes and clinical response to oncological treatment. Finally, we show that the extracted features can be used alongside unsupervised learning methods to analyze genomic samples.

Date of online publication

05.08.2022

Pages (from - to)

4466 - 4473

DOI

10.1093/bioinformatics/btac513

URL

https://academic.oup.com/bioinformatics/article-abstract/38/19/4466/6656344

Ministry points / journal

200.0

Impact Factor

6.931 [List 2021]

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