Deep learning for computational biology

Abstract

Technological advances in genomics and imaging have led to an explosion of molecular and cellular profiling data from large numbers of samples. This rapid increase in biological data dimension and acquisition rate is challenging conventional analysis strategies. Modern machine learning methods, such as deep learning, promise to leverage very large data sets for finding hidden structure within them, and for making accurate predictions. In this review, we discuss applications of this new breed of analysis approaches in regulatory genomics and cellular imaging. We provide background of what deep learning is, and the settings in which it can be successfully applied to derive biological insights. In addition to presenting specific applications and providing tips for practical use, we also highlight possible pitfalls and limitations to guide computational biologists when and how to make the most use of this new technology.

Cited by papers we hold 34

• A technical review of multi-omics data integration methods: from classical statistical to deep generative approaches · 2025
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• GeneCompass: deciphering universal gene regulatory mechanisms with a knowledge-informed cross-species foundation model · 2024
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• Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development · 2023
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• Lung adenocarcinoma-related target gene prediction and drug repositioning · 2022
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• Current progress and open challenges for applying deep learning across the biosciences · 2022
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• Deep learning for cancer type classification and driver gene identification · 2021
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• Performance improvement for a 2D convolutional neural network by using SSC encoding on protein–protein interaction tasks · 2021
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• Machine Learning and Natural Language Processing in Mental Health: Systematic Review · 2021
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• A deep learning approach to programmable RNA switches · 2020
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• Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis · 2020
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• Systems Biology of Gastric Cancer: Perspectives on the Omics-Based Diagnosis and Treatment · 2020
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• Model-driven generation of artificial yeast promoters · 2020
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• Knowledge-primed neural networks enable biologically interpretable deep learning on single-cell sequencing data · 2020
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• A Brief History of Protein Sorting Prediction · 2019
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• Machine and deep learning meet genome-scale metabolic modeling · 2019
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• Predicting drug response of tumors from integrated genomic profiles by deep neural networks · 2019
  Corrected
• Phenotype Prediction and Genome-Wide Association Study Using Deep Convolutional Neural Network of Soybean · 2019
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• Benchmarking Parametric and Machine Learning Models for Genomic Prediction of Complex Traits · 2019
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• Scaling computational genomics to millions of individuals with GPUs · 2019
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• Taxonomy metagenomic analysis for microbial sequences in three domains system via machine learning approaches · 2018
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and 14 more

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