A guide to machine learning for biologists

CryoDRGN: reconstruction of heterogeneous cryo-EM structures using neural networks

(2021) Ellen D. Zhong et al.   NATURE METHODS

Improved protein structure refinement guided by deep learning based accuracy estimation

(2021) Naozumi Hiranuma et al.   Nature Communications

Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations

(2021) Payel Das et al.   Nature Biomedical Engineering

scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses

(2021) Juexin Wang et al.   Nature Communications

Highly accurate protein structure prediction with AlphaFold

(2021) John Jumper et al.   NATURE

Structure-based protein function prediction using graph convolutional networks

(2021) Vladimir Gligorijević et al.   Nature Communications

Improved protein structure prediction using potentials from deep learning

(2020) Andrew W. Senior et al.   NATURE

Improved protein structure prediction using predicted interresidue orientations

(2020) Jianyi Yang et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Standard machine learning approaches outperform deep representation learning on phenotype prediction from transcriptomics data

(2020) Aaron M. Smith et al.   BMC BIOINFORMATICS

A Deep Learning Approach to Antibiotic Discovery

(2020) Jonathan M. Stokes et al.   CELL

Deep Learning to Predict Protein Backbone Structure from High-Resolution Cryo-EM Density Maps

(2020) Dong Si et al.   Scientific Reports

Using deep mutational scanning to benchmark variant effect predictors and identify disease mutations

(2020) Benjamin J Livesey et al.   Molecular Systems Biology

Applications of machine learning to diagnosis and treatment of neurodegenerative diseases

(2020) Monika A. Myszczynska et al.   Nature Reviews Neurology

Deep learning for genomics using Janggu

(2020) Wolfgang Kopp et al.   Nature Communications

A deep learning model to predict RNA-Seq expression of tumours from whole slide images

(2020) Benoît Schmauch et al.   Nature Communications

A Generative Neural Network for Maximizing Fitness and Diversity of Synthetic DNA and Protein Sequences

(2020) Johannes Linder et al.   Cell Systems

Machine Learning Applications for Mass Spectrometry-Based Metabolomics

(2020) Ulf W. Liebal et al.   Metabolites

Enhancing scientific discoveries in molecular biology with deep generative models

(2020) Romain Lopez et al.   Molecular Systems Biology

Predicting 3D genome folding from DNA sequence with Akita

(2020) Geoff Fudenberg et al.   NATURE METHODS

Fast and Flexible Protein Design Using Deep Graph Neural Networks

(2020) Alexey Strokach et al.   Cell Systems

DeMaSk: a deep mutational scanning substitution matrix and its use for variant impact prediction

(2020) Daniel Munro et al.   BIOINFORMATICS

nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation

(2020) Fabian Isensee et al.   NATURE METHODS

SIRIUS 4: a rapid tool for turning tandem mass spectra into metabolite structure information

(2019) Kai Dührkop et al.   NATURE METHODS

Selene: a PyTorch-based deep learning library for sequence data

(2019) Kathleen M. Chen et al.   NATURE METHODS

Protein model quality assessment using 3D oriented convolutional neural networks

(2019) Guillaume Pagès et al.   BIOINFORMATICS

The Kipoi repository accelerates community exchange and reuse of predictive models for genomics

(2019) Žiga Avsec et al.   NATURE BIOTECHNOLOGY

The PSIPRED Protein Analysis Workbench: 20 years on

(2019) Daniel W A Buchan et al.   NUCLEIC ACIDS RESEARCH

End-to-End Differentiable Learning of Protein Structure

(2019) Mohammed AlQuraishi   Cell Systems

ProteinNet: a standardized data set for machine learning of protein structure

(2019) Mohammed AlQuraishi   BMC BIOINFORMATICS

Privacy-Preserving Generative Deep Neural Networks Support Clinical Data Sharing

(2019) Brett K. Beaulieu-Jones et al.  

Machine-learning-guided directed evolution for protein engineering

(2019) Kevin K. Yang et al.   NATURE METHODS

Machine learning approaches and their current application in plant molecular biology: A systematic review

(2019) Jose Cleydson F. Silva et al.   PLANT SCIENCE

Environmental conditions shape the nature of a minimal bacterial genome

(2019) Magdalena Antczak et al.   Nature Communications

HyperFoods: Machine intelligent mapping of cancer-beating molecules in foods

(2019) Kirill Veselkov et al.   Scientific Reports

Ten quick tips for effective dimensionality reduction

(2019) Lan Huong Nguyen et al.   PLoS Computational Biology

Deep Neural Networks for Classification of LC-MS Spectral Peaks

(2019) Edward D. Kantz et al.   ANALYTICAL CHEMISTRY

HH-suite3 for fast remote homology detection and deep protein annotation

(2019) Martin Steinegger et al.   BMC BIOINFORMATICS

Deep learning enables rapid identification of potent DDR1 kinase inhibitors

(2019) Alex Zhavoronkov et al.   NATURE BIOTECHNOLOGY

Robust and automated detection of subcellular morphological motifs in 3D microscopy images

(2019) Meghan K. Driscoll et al.   NATURE METHODS

Setting the standards for machine learning in biology

(2019) David T. Jones   NATURE REVIEWS MOLECULAR CELL BIOLOGY

Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning

(2019) Frank Noé et al.   SCIENCE

Real-time cryo-electron microscopy data preprocessing with Warp

(2019) Dimitry Tegunov et al.   NATURE METHODS

Unified rational protein engineering with sequence-based deep representation learning

(2019) Ethan C. Alley et al.   NATURE METHODS

Critical assessment of methods of protein structure prediction (CASP)—Round XIII

(2019) Andriy Kryshtafovych et al.   PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS

The art of using t-SNE for single-cell transcriptomics

(2019) Dmitry Kobak et al.   Nature Communications

Gene Expression Value Prediction Based on XGBoost Algorithm

(2019) Wei Li et al.   Frontiers in Genetics

Visualizing structure and transitions in high-dimensional biological data

(2019) Kevin R. Moon et al.   NATURE BIOTECHNOLOGY

Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning

(2019) P. Gainza et al.   NATURE METHODS

Deep learning for inferring gene relationships from single-cell expression data

(2019) Ye Yuan et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

deepNF: deep network fusion for protein function prediction

(2018) Vladimir Gligorijević et al.   BIOINFORMATICS

Modeling polypharmacy side effects with graph convolutional networks

(2018) Marinka Zitnik et al.   BIOINFORMATICS

Prediction of enhancer-promoter interactions via natural language processing

(2018) Wanwen Zeng et al.   BMC GENOMICS

Simulations meet machine learning in structural biology

(2018) Adrià Pérez et al.   CURRENT OPINION IN STRUCTURAL BIOLOGY

Recurrent Neural Network Model for Constructive Peptide Design

(2018) Alex T. Müller et al.   Journal of Chemical Information and Modeling

Opportunities and obstacles for deep learning in biology and medicine

(2018) Travers Ching et al.   Journal of the Royal Society Interface

Points of significance: Machine learning: supervised methods

(2018) Danilo Bzdok et al.   NATURE METHODS

Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning

(2018) Ryan Poplin et al.   Nature Biomedical Engineering

A universal SNP and small-indel variant caller using deep neural networks

(2018) Ryan Poplin et al.   NATURE BIOTECHNOLOGY

Inferring single-trial neural population dynamics using sequential auto-encoders

(2018) Chethan Pandarinath et al.   NATURE METHODS

Design of metalloproteins and novel protein folds using variational autoencoders

(2018) Joe G. Greener et al.   Scientific Reports

A primer on deep learning in genomics

(2018) James Zou et al.   NATURE GENETICS

CATH: expanding the horizons of structure-based functional annotations for genome sequences

(2018) Ian Sillitoe et al.   NUCLEIC ACIDS RESEARCH

Multi-omic and multi-view clustering algorithms: review and cancer benchmark

(2018) Nimrod Rappoport et al.   NUCLEIC ACIDS RESEARCH

Realizing private and practical pharmacological collaboration

(2018) Brian Hie et al.   SCIENCE

Trajectory-based training enables protein simulations with accurate folding and Boltzmann ensembles in cpu-hours

(2018) John M. Jumper et al.   PLoS Computational Biology

Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study

(2018) John R. Zech et al.   PLOS MEDICINE

Structural and Sequence Similarity Makes a Significant Impact on Machine-Learning-Based Scoring Functions for Protein–Ligand Interactions

(2017) Yang Li et al.   Journal of Chemical Information and Modeling

Dermatologist-level classification of skin cancer with deep neural networks

(2017) Andre Esteva et al.   NATURE

Mutation effects predicted from sequence co-variation

(2017) Thomas A Hopf et al.   NATURE BIOTECHNOLOGY

MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets

(2017) Martin Steinegger et al.   NATURE BIOTECHNOLOGY

Points of Significance: Clustering

(2017) Naomi Altman et al.   NATURE METHODS

Ten Simple Rules for Developing Usable Software in Computational Biology

(2017) Markus List et al.   PLoS Computational Biology

Convolutional neural network architectures for predicting DNA–protein binding

(2016) Haoyang Zeng et al.   BIOINFORMATICS

Learning a hierarchical representation of the yeast transcriptomic machinery using an autoencoder model

(2016) Lujia Chen et al.   BMC BIOINFORMATICS

Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks

(2016) David R. Kelley et al.   GENOME RESEARCH

Improving scoring-docking-screening powers of protein-ligand scoring functions using random forest

(2016) Cheng Wang et al.   JOURNAL OF COMPUTATIONAL CHEMISTRY

DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences

(2016) Daniel Quang et al.   NUCLEIC ACIDS RESEARCH

Toward an Integration of Deep Learning and Neuroscience

(2016) Adam H. Marblestone et al.   Frontiers in Computational Neuroscience

Predicting DNA Methylation State of CpG Dinucleotide Using Genome Topological Features and Deep Networks

(2016) Yiheng Wang et al.   Scientific Reports

FFPred 3: feature-based function prediction for all Gene Ontology domains

(2016) Domenico Cozzetto et al.   Scientific Reports

Correct machine learning on protein sequences: a peer-reviewing perspective

(2015) Ian Walsh et al.   BRIEFINGS IN BIOINFORMATICS

Deep learning

(2015) Yann LeCun et al.   NATURE

Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning

(2015) Babak Alipanahi et al.   NATURE BIOTECHNOLOGY

Machine learning applications in genetics and genomics

(2015) Maxwell W. Libbrecht et al.   NATURE REVIEWS GENETICS

Prediction of Druggable Proteins Using Machine Learning and Systems Biology: A Mini-Review

(2015) Gaurav Kandoi et al.   Frontiers in Physiology

Building Predictive Models inRUsing thecaretPackage

(2015) Max Kuhn   Journal of Statistical Software

A general framework for estimating the relative pathogenicity of human genetic variants

(2014) Martin Kircher et al.   NATURE GENETICS

DUET: a server for predicting effects of mutations on protein stability using an integrated computational approach

(2014) D. E. V. Pires et al.   NUCLEIC ACIDS RESEARCH

ECOD: An Evolutionary Classification of Protein Domains

(2014) Hua Cheng et al.   PLoS Computational Biology

The Role of Balanced Training and Testing Data Sets for Binary Classifiers in Bioinformatics

(2013) Qiong Wei et al.   PLoS One

Predicting folding free energy changes upon single point mutations

(2012) Zhe Zhang et al.   BIOINFORMATICS

Protein sequence comparison and fold recognition: progress and good-practice benchmarking

(2011) Johannes Söding et al.   CURRENT OPINION IN STRUCTURAL BIOLOGY

Transmembrane protein topology prediction using support vector machines

(2009) Timothy Nugent et al.   BMC BIOINFORMATICS

Data clustering: 50 years beyond K-means

(2009) Anil K. Jain   PATTERN RECOGNITION LETTERS

Support Vector Machines and Kernels for Computational Biology

(2008) Asa Ben-Hur et al.   PLoS Computational Biology