DDBJ Data Analysis Challenge: a machine learning competition to predict <i>Arabidopsis</i> chromatin feature annotations from DNA sequences

Meeting report

DDBJ Data Analysis Challenge: a machine learning competition to predict Arabidopsis chromatin feature annotations from DNA sequences

Eli Kaminuma , Yukino Baba, Masahiro Mochizuki, Hirotaka Matsumoto, Haruka Ozaki, Toshitsugu Okayama, Takuya Kato, Shinya Oki, Takatomo Fujisawa, Yasukazu Nakamura, Masanori Arita, Osamu Ogasawara, Hisashi Kashima, Graduate School of Science, The University of Tokyo">Toshihisa Takagi

Author information

Eli Kaminuma Corresponding author
Center for Information Biology, National Institute of Genetics
Yukino Baba
Graduate School of Informatics, Kyoto University
Masahiro Mochizuki
IMSBIO Co., Ltd.
Hirotaka Matsumoto
Laboratory for Bioinformatics Research, RIKEN Center for Biosystems Dynamics Research
Haruka Ozaki
Laboratory for Bioinformatics Research, RIKEN Center for Biosystems Dynamics Research
Toshitsugu Okayama
BITS Co., Ltd.
Takuya Kato
Graduate School of Information Science and Technology, The University of Tokyo
Shinya Oki
Graduate School of Medical Sciences, Kyushu University
Takatomo Fujisawa
Center for Information Biology, National Institute of Genetics
Yasukazu Nakamura
Center for Information Biology, National Institute of Genetics
Masanori Arita
Center for Information Biology, National Institute of Genetics
Osamu Ogasawara
Center for Information Biology, National Institute of Genetics
Hisashi Kashima
Graduate School of Informatics, Kyoto University
Toshihisa Takagi
Center for Information Biology, National Institute of Genetics
Graduate School of Science, The University of Tokyo

Keywords: chromatin features prediction, deep learning, machine learning competition, sequence read archive

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Supplementary material

2020 Volume 95 Issue 1 Pages 43-50

DOI https://doi.org/10.1266/ggs.19-00034

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Abstract

Recently, the prospect of applying machine learning tools for automating the process of annotation analysis of large-scale sequences from next-generation sequencers has raised the interest of researchers. However, finding research collaborators with knowledge of machine learning techniques is difficult for many experimental life scientists. One solution to this problem is to utilise the power of crowdsourcing. In this report, we describe how we investigated the potential of crowdsourced modelling for a life science task by conducting a machine learning competition, the DNA Data Bank of Japan (DDBJ) Data Analysis Challenge. In the challenge, participants predicted chromatin feature annotations from DNA sequences with competing models. The challenge engaged 38 participants, with a cumulative total of 360 model submissions. The performance of the top model resulted in an area under the curve (AUC) score of 0.95. Over the course of the competition, the overall performance of the submitted models improved by an AUC score of 0.30 from the first submitted model. Furthermore, the 1^st- and 2^nd-ranking models utilised external data such as genomic location and gene annotation information with specific domain knowledge. The effect of incorporating this domain knowledge led to improvements of approximately 5%–9%, as measured by the AUC scores. This report suggests that machine learning competitions will lead to the development of highly accurate machine learning models for use by experimental scientists unfamiliar with the complexities of data science.

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