Detailed Information on Publication Record
2023
Genomic benchmarks: a collection of datasets for genomic sequence classification
GREŠOVÁ, Katarína, Vlastimil MARTINEK, David ČECHÁK, Petr ŠIMEČEK, Panagiotis ALEXIOU et. al.Basic information
Original name
Genomic benchmarks: a collection of datasets for genomic sequence classification
Authors
GREŠOVÁ, Katarína (703 Slovakia, belonging to the institution), Vlastimil MARTINEK (203 Czech Republic, belonging to the institution), David ČECHÁK (203 Czech Republic, belonging to the institution), Petr ŠIMEČEK (203 Czech Republic, guarantor, belonging to the institution) and Panagiotis ALEXIOU (300 Greece, belonging to the institution)
Edition
BMC Genomic Data, 2730-6844, BMC, 2023, 2730-6844
Other information
Language
English
Type of outcome
Článek v odborném periodiku
Field of Study
10610 Biophysics
Country of publisher
United Kingdom of Great Britain and Northern Ireland
Confidentiality degree
není předmětem státního či obchodního tajemství
References:
Impact factor
Impact factor: 1.900 in 2022
RIV identification code
RIV/00216224:14740/23:00131330
Organization unit
Central European Institute of Technology
UT WoS
000981254200001
Keywords in English
Genomics; Dataset; Benchmark; Deep learning; Convolutional neural network
Tags
International impact, Reviewed
Změněno: 8/4/2024 10:34, Mgr. Eva Dubská
Abstract
V originále
Background Recently, deep neural networks have been successfully applied in many biological fields. In 2020, a deep learning model AlphaFold won the protein folding competition with predicted structures within the error tolerance of experimental methods. However, this solution to the most prominent bioinformatic challenge of the past 50 years has been possible only thanks to a carefully curated benchmark of experimentally predicted protein structures. In Genomics, we have similar challenges (annotation of genomes and identification of functional elements) but currently, we lack benchmarks similar to protein folding competition. Results Here we present a collection of curated and easily accessible sequence classification datasets in the field of genomics. The proposed collection is based on a combination of novel datasets constructed from the mining of publicly available databases and existing datasets obtained from published articles. The collection currently contains nine datasets that focus on regulatory elements (promoters, enhancers, open chromatin region) from three model organisms: human, mouse, and roundworm. A simple convolution neural network is also included in a repository and can be used as a baseline model. Benchmarks and the baseline model are distributed as the Python package ‘genomic-benchmarks’, and the code is available at https://github.com/ML-Bioinfo-CEITEC/genomic_benchmarks. Conclusions Deep learning techniques revolutionized many biological fields but mainly thanks to the carefully curated benchmarks. For the field of Genomics, we propose a collection of benchmark datasets for the classification of genomic sequences with an interface for the most commonly used deep learning libraries, implementation of the simple neural network and a training framework that can be used as a starting point for future research. The main aim of this effort is to create a repository for shared datasets that will make machine learning for genomics more comparable and reproducible while reducing the overhead of researchers who want to enter the field, leading to healthy competition and new discoveries.
Links
| GF23-04260L, research and development project |
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| LM2018140, research and development project |
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| 4431, interní kód MU |
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| 867414, interní kód MU |
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| 896172, interní kód MU |
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| 90267, large research infrastructures |
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