A guide to machine learning for biologists

2021/09/13 by Joe G. Greener, Shaun M. Kandathil, Lewis Moffat +1 · 34 citations
Biochemistry, Genetics and Molecular Biology · #Cell Image Analysis Techniques #Genetics, Bioinformatics, and Biomedical Research #Machine Learning in Bioinformatics

paper · doi:10.1038/s41580-021-00407-0

crossref issued 2021/09/13 · crossref published 2021/09/13 · crossref published-online 2021/09/13 · openalex publication_date 2021/09/13 · crossref created 2021/09/13 · crossref published-print 2022/01/01 · crossref deposited 2024/09/08 · openalex created_date 2025/10/10 · crossref indexed 2026/07/30 · openalex updated_date 2026/07/31

Citations

• Predicting the secondary structure of globular proteins using neural network models
• Federated Optimization: Distributed Machine Learning for On-Device Intelligence
• Sanity Checks for Saliency Maps
• Fast Graph Representation Learning with PyTorch Geometric
• Evaluating Protein Transfer Learning with TAPE
• TensorFlow: A system for large-scale machine learning
• Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Improved protein structure prediction using predicted interresidue orientations
• Improved protein structure prediction using potentials from deep learning
• A Deep Learning Approach to Antibiotic Discovery
• nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
• Shortcut learning in deep neural networks
• Applications of machine learning to diagnosis and treatment of neurodegenerative diseases
• Highly accurate protein structure prediction with AlphaFold

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• Generative AI for synthetic biology: Designing biological parts, circuits, and genomes
• Finding functional motifs in protein sequences with deep learning and natural language models
• Connecting the dots: deep learning-based automated model building methods in cryo-EM
• Discovery of predictive biomarkers for cancer therapy through computational approaches
• Functional residuomics – analyzing how missense mutations impact cellular systems
• Predictive models aren't for causal inference
• Associations Between Thyroid Function and Periodontitis: A Machine Learning Approach Using NHANES
• Antibacterial Drug Discovery: Deep Learning Successes and Challenges through the Structural Biology Lens
• Decoding functional genes across species with annotation-independent machine learning
• Getting Started with Machine Learning for Experimental Biochemists and Other Molecular Scientists
• Demystifying the black box: A survey on explainable artificial intelligence (XAI) in bioinformatics
• Machine learning for synthetic gene circuit engineering
• MechanoAge, a machine learning platform to identify individuals susceptible to breast cancer based on mechanical properties of single cells
• Using weak signals to predict spontaneous breathing trial success: a machine learning approach
• Induced pluripotent stem cells (iPSCs): molecular mechanisms of induction and applications
• Sequential active learning for medium optimization in mAb production
• Predicting PROTAC off-target effects via warhead involvement levels in drug–target interactions using graph attention neural networks
• Metabolic signatures for gastric cancer diagnosis and mechanistic insights: a multicenter study
• A Guide to Metabolic Network Modeling for Plant Biology
• Practical machine learning model selection and interpretation for organophosphorus flame retardancy in Epoxy resin
• Artificial intelligence: the human response to approach the complexity of big data in biology
• Quantum Computing for Molecular Biology**
• Morphological Species Delimitation in The Western Pond Turtle (Actinemys): Can Machine Learning Methods Aid in Cryptic Species Identification?
• The Evolution of Spatial Omics Technologies Introduces A Novel Avenue for Lung Cancer Research
• Harnessing the power of artificial intelligence to advance cell therapy
• An End to End Edge to Cloud Data and Analytics Strategy
• Machine learning-assisted structure annotation of natural products based on MS and NMR data
• Challenges in developing cell culture media using machine learning
• Mechanobiology of inflammation: Pulling the strings of innate immunity
• Flow Matching Meets Biology and Life Science: A Survey
• Biology-aware machine learning for culture medium optimization
• How did we get there? AI applications to biological networks and sequences
• Best Practices for Machine Learning-Assisted Protein Engineering