A guide to machine learning for biologists
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Summary
This Review provides a gentle introduction to a few key machine learning techniques, including the most recently developed and widely used techniques involving deep neural networks.
- Type
- review
- Published
- 2021-09-13
- Cited by
- 1,765
- References
- 173
- Access
- Open access
- OpenAlex
- https://openalex.org/W3200707343
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:237504441
Keywords
Machine learning, Artificial intelligence, Computer science, Biological data, Deep learning
References
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- Building Predictive Models in R Using the caret Package
- The Role of Balanced Training and Testing Data Sets for Binary Classifiers in Bioinformatics
- ECOD: An Evolutionary Classification of Protein Domains
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction
- Addendum: Regularization and variable selection via the elastic net
- The recent excitement about neural networks
- Data clustering: 50 years beyond K-means
- Understanding Protein Flexibility through Dimensionality Reduction
- SPICKER: A clustering approach to identify near‐native protein folds
- DUET: a server for predicting effects of mutations on protein stability using an integrated computational approach
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition by Trevor Hastie, Robert Tibshirani, Jerome Friedman
- Neural-network-based classification of cognitively normal, demented, Alzheimer disease and vascular dementia from single photon emission with computed tomography image data from brain.
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- Optimized strategy for schistosomiasis elimination: results from marginal benefit modeling
- Ten quick tips for deep learning in biology
- Machine learning methods for prediction of cancer driver genes: a survey paper
- Modelling the coupling of the M-clock and C-clock in lymphatic muscle cells
- Towards the De Novo Design of HIV-1 Protease Inhibitors Based on Natural Products
- The impact of AlphaFold2 one year on
- Machine Learning for Auto-segmentation in Radiotherapy Planning.
- Predictive analytics with ensemble modeling in laparoscopic surgery: A technical note
- Turning the Knobs: The Impact of Post-translational Modifications on Carbon Metabolism
- Computational prediction of plant metabolic pathways.
- The identification of microplastics based on vibrational spectroscopy data – a critical review of data analysis routines
- An approachable, flexible and practical machine learning workshop for biologists
- Computational Models for Clinical Applications in Personalized Medicine—Guidelines and Recommendations for Data Integration and Model Validation
- Hybrid HCNN-KNN Transfer Learning Model Enhances Age Estimation Accuracy in Orthopantomography
- Deep learning approach identified a gene signature predictive of the severity of renal damage caused by chronic cadmium accumulation.
- Deep learning shapes single-cell data analysis
- Enabling interpretable machine learning for biological data with reliability scores
- Resolving Protein Conformational Plasticity and Substrate Binding Through the Lens of Machine-Learning
- Fighting fire with fire: deploying complexity in computational modeling to effectively characterize complex biological systems.
- Efficient Exploration of Sequence Space by Sequence-Guided Protein Engineering and Design.
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