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COMPUTATIONAL GENOMICS

Neural network architecture search with AMBER

Nature Machine Intelligence volume 3, pages 372–373 (2021)Cite this article

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Deep learning applied to genomics can learn patterns in biological sequences, but designing such models requires expertise and effort. Recent work demonstrates the efficiency of a neural network architecture search algorithm in optimizing genomic models.

Deep learning has been powerful in learning complex functions from data and has been applied in computer vision, natural language processing and biology. If we view the human genome as a book with three billion letters of nucleotides represented by A, C, G and T, genes and gene-controlling sequences are encoded in the book and variations in the genome can link to disease conditions. Neural network models that extract patterns from the sequences can help predict functional genomic elements and interpret genetic variations. However, the current deep learning models for genomics usually involve expert-designed neural network structures and require extensive tuning, making such models unapproachable for most other scientists. In a recent publication in Nature Machine Intelligence, Zhang and colleagues1 present a framework called Automated Modelling for Biological Evidence-based Research (AMBER), which incorporates a deep learning architecture searching algorithm and demonstrates efficient and automatic model selection for genomic problems.

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Fig. 1: Neural architecture search for genomic problems.

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Authors and Affiliations

  1. Department of Data Science, Dana-Farber Cancer Institute, Harvard T. H. Chan School of Public Health, Boston, MA, USA

    Yi Zhang, Yang Liu & X. Shirley Liu

Authors
  1. Yi Zhang
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  2. Yang Liu
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  3. X. Shirley Liu
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Corresponding author

Correspondence to X. Shirley Liu.

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Competing interests

X.S.L. is a cofounder, board member, SAB, and consultant of GV20 Oncotherapy and its subsidiaries, SAB of 3DMedCare, consultant for Genentech, and stockholder of AMGN, JNJ, MRK, PFE, and receives sponsored research funding from Takeda and Sanofi.

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Zhang, Y., Liu, Y. & Liu, X.S. Neural network architecture search with AMBER. Nat Mach Intell 3, 372–373 (2021). https://doi.org/10.1038/s42256-021-00350-x

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