Abdul Muntakim Rafi
Дарья Ногина
Dohoon Lee
Danyeong Lee
Nayeon Kim
Sangyeup Kim
Dohyeon Kim
Yeojin Shin
Il-Youp Kwak
Георгий Мещеряков
Андрей Ландо
Арсений Зинкевич
Byeong-Chan Kim
Juhyun Lee
Taein Kang
Eeshit Dhaval Vaishnav
Payman Yadollahpour
Random Promoter DREAM Challenge Consortium
Sun Kim
Jake Albrecht
Aviv Regev
Wuming Gong
Иван Кулаковский
Pablo Meyer
Carl G. de Boer
A community effort to optimize sequence-based deep learning models of gene regulation
Аннотация
A systematic evaluation of how model architectures and training strategies impact genomics model performance is needed. To address this gap, we held a DREAM Challenge where competitors trained models on a dataset of millions of random promoter DNA sequences and corresponding expression levels, experimentally determined in yeast. For a robust evaluation of the models, we designed a comprehensive suite of benchmarks encompassing various sequence types. All top-performing models used neural networks but diverged in architectures and training strategies. To dissect how architectural and training choices impact performance, we developed the Prix Fixe framework to divide models into modular building blocks. We tested all possible combinations for the top three models, further improving their performance. The DREAM Challenge models not only achieved state-of-the-art results on our comprehensive yeast dataset but also consistently surpassed existing benchmarks on Drosophila and human genomic datasets, demonstrating the progress that can be driven by gold-standard genomics datasets.
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