Genetic Architect: Discovering Genomic Structure with Learned Neural Architectures

By Laura Deming, Sasha Targ, N. Sauder, Diogo Almeida, Chun Jimmie Ye. 2016

14 citations recorded by the source corpus.

Each human genome is a 3 billion base pair set of encoding instructions. Decoding the genome using deep learning fundamentally differs from most tasks, as we do not know the full structure of the data and therefore cannot design architectures to suit it. As such, architectures that fit the structure of genomics should be learned not prescribed. Here, we develop a novel search algorithm, applicable across domains, that discovers an optimal architecture which simultaneously learns general genomic patterns and identifies the most important sequence motifs in predicting functional genomic outcomes. The architectures we find using this algorithm succeed at using only RNA expression data to predict gene regulatory structure, learn human-interpretable visualizations of key sequence motifs, and surpass state-of-the-art results on benchmark genomics challenges.

Subfields: Molecular Biology

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In Molecular Biology

Citations in the corpus

Opportunities and obstacles for deep learning in biology and medicine (2017, methodology): “[352] applied the attention mechanism to models trained on genomic sequence.”
Explainable Machine Learning for Scientific Insights and Discoveries (2019, background): “For example, [74] applies attention modules in NNs trained on genomic sequences for the identification of important sequence motifs by visualizing the attention mask weights.”
Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification (2018, methodology): “[49] applied the attention mechanism to models trained on genomic sequence.”