Deep learning: new computational modelling techniques for genomics

2019/04/10 by Gökçen Eraslan, Gökcen Eraslan, Žiga Avsec +2 · 1 voice · 20 citations
Biochemistry, Genetics and Molecular Biology · #Gene expression and cancer classification #Genomics and Chromatin Dynamics #RNA modifications and cancer

paper · doi:10.1038/s41576-019-0122-6

openalex publication_date 2019/04/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30

Citations

• Deep learning in neural networks: An overview
• Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)
• Taking the Human Out of the Loop: A Review of Bayesian Optimization
• Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
• An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
• Molecular graph convolutions: moving beyond fingerprints

Cited by

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• Uncovering co-regulatory modules and gene regulatory networks in the heart through machine learning-based analysis of large-scale epigenomic data
• Metabolomics in the era of artificial intelligence
• Effective gene expression prediction from sequence by integrating long-range interactions
• Class-Conditional VAE-GAN for Local-Ancestry Simulation
• Identification of mobile genetic elements with geNomad
• Modelling and design of transcriptional enhancers
• Toward a foundation model of causal cell and tissue biology with a Perturbation Cell and Tissue Atlas
• Patient Similarity Analysis with Longitudinal Health Data
• How Artificial Intelligence is Shaping Life Sciences: Systematic Qualitative Review with a Future-oriented Analytical Framework
• Explainable Multimodal Machine Learning Using Combined Environmental DNA and Biogeographic Features for Ecosystem Biomonitoring
• Transformers and genome language models
• Benchmarking atlas-level data integration in single-cell genomics
• WILDS: A Benchmark of in-the-Wild Distribution Shifts
• CodonMoE: DNA Language Models for mRNA Analyses
• GenoML: Automated Machine Learning for Genomics

Discussions

• Eraslan G, Avsec Ž, Gagneur J, Theis FJ. Deep learning: new computational modelling techniques for genomics. Nat Rev Genet 2019. doi.org/10.1038/s415.... [bsky, 6 points, 2 comments]