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@article{2300026,
author = {Grešová, Katarína and Martinek, Vlastimil and Čechák, David and Šimeček, Petr and Alexiou, Panagiotis},
article_location = {2730-6844},
article_number = {1},
doi = {http://dx.doi.org/10.1186/s12863-023-01123-8},
keywords = {Genomics; Dataset; Benchmark; Deep learning; Convolutional neural network},
language = {eng},
issn = {2730-6844},
journal = {BMC Genomic Data},
title = {Genomic benchmarks: a collection of datasets for genomic sequence classification},
url = {https://link.springer.com/article/10.1186/s12863-023-01123-8},
volume = {24},
year = {2023}
}
TY - JOUR
ID - 2300026
AU - Grešová, Katarína - Martinek, Vlastimil - Čechák, David - Šimeček, Petr - Alexiou, Panagiotis
PY - 2023
TI - Genomic benchmarks: a collection of datasets for genomic sequence classification
JF - BMC Genomic Data
VL - 24
IS - 1
SP - 1-9
EP - 1-9
PB - BMC
SN - 27306844
KW - Genomics
KW - Dataset
KW - Benchmark
KW - Deep learning
KW - Convolutional neural network
UR - https://link.springer.com/article/10.1186/s12863-023-01123-8
N2 - 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.
ER -
GREŠOVÁ, Katarína, Vlastimil MARTINEK, David ČECHÁK, Petr ŠIMEČEK and Panagiotis ALEXIOU. Genomic benchmarks: a collection of datasets for genomic sequence classification. \textit{BMC Genomic Data}. 2730-6844: BMC, 2023, vol.~24, No~1, p.~1-9. ISSN~2730-6844. Available from: https://dx.doi.org/10.1186/s12863-023-01123-8.
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