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Machine learning for microbiologists

Nature reviews. Microbiology, 2024-04, Vol.22 (4), p.191-205

2024

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Improved protein structure prediction using potentials from deep learning

Nature (London), 2020-01, Vol.577 (7792), p.706-710

2020

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Applications of machine learning in drug discovery and development

Nature reviews. Drug discovery, 2019-06, Vol.18 (6), p.463-477

2019

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All-optical spiking neurosynaptic networks with self-learning capabilities

Nature (London), 2019-05, Vol.569 (7755), p.208-214

2019

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Deep learning for regulatory genomics

Nature biotechnology, 2015-08, Vol.33 (8), p.825-826

2015

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Three pitfalls to avoid in machine learning

Nature (London), 2019-08, Vol.572 (7767), p.27-29

Riley, Patrick

2019

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Deep learning takes on tumours

Nature (London), 2020-04, Vol.580 (7804), p.551-553

Landhuis, Esther

2020

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Reconstructing cell cycle and disease progression using deep learning

Nature communications, 2017-09, Vol.8 (1), p.463-6, Article 463

2017

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Hierarchical graph learning for protein-protein interaction

Nature communications, 2023-02, Vol.14 (1), p.1093-1093, Article 1093

2023

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Neuroinspired unsupervised learning and pruning with subquantum CBRAM arrays

Nature communications, 2018-12, Vol.9 (1), p.5312-11, Article 5312

2018

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Improved protein structure refinement guided by deep learning based accuracy estimation

Nature communications, 2021-02, Vol.12 (1), p.1340-1340, Article 1340

2021

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

Nature biotechnology, 2015-08, Vol.33 (8), p.831-838

2015

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Dictionary learning for integrative, multimodal and scalable single-cell analysis

Nature biotechnology, 2024-02, Vol.42 (2), p.293-304

2024

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Humans monitor learning progress in curiosity-driven exploration

Nature communications, 2021-10, Vol.12 (1), p.5972-5972, Article 5972

2021

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Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learning

Nature communications, 2021-05, Vol.12 (1), p.3238-3238, Article 3238

2021

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Toward a unified framework for interpreting machine-learning models in neuroimaging

Nature protocols, 2020-04, Vol.15 (4), p.1399-1435

2020

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iBEAT V2.0: a multisite-applicable, deep learning-based pipeline for infant cerebral cortical surface reconstruction

Nature protocols, 2023-05, Vol.18 (5), p.1488-1509

2023

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The touchscreen operant platform for testing learning and memory in rats and mice

Nature protocols, 2013-10, Vol.8 (10), p.1961-1984

2013

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Deep learning-assisted comparative analysis of animal trajectories with DeepHL

Nature communications, 2020-10, Vol.11 (1), p.5316-5316, Article 5316

2020

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A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei

Nature protocols, 2021-02, Vol.16 (2), p.754-774

2021

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A versatile active learning workflow for optimization of genetic and metabolic networks

Nature communications, 2022-07, Vol.13 (1), p.3876-3876, Article 3876

2022

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Clearing Opacity Through Machine Learning

Iowa law review, 2021-01, Vol.106 (2), p.775-812

2021

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Deep learning the structural determinants of protein biochemical properties by comparing structural ensembles with DiffNets

Nature communications, 2021-05, Vol.12 (1), p.3023-3023, Article 3023

2021

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Machine Learning as Natural Monopoly

Iowa law review, 2022-05, Vol.107 (4), p.1543-1614

Narechania, Tejas N

2022

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Deep learning powers a motion-tracking revolution

Nature (London), 2019-10, Vol.574 (7776), p.137-138

Kwok, Roberta

2019

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Machine learning for microbiologists

Improved protein structure prediction using potentials from deep learning

Applications of machine learning in drug discovery and development

All-optical spiking neurosynaptic networks with self-learning capabilities

Deep learning for regulatory genomics

Three pitfalls to avoid in machine learning

Deep learning takes on tumours

Reconstructing cell cycle and disease progression using deep learning

Hierarchical graph learning for protein-protein interaction

Neuroinspired unsupervised learning and pruning with subquantum CBRAM arrays

Improved protein structure refinement guided by deep learning based accuracy estimation

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

Dictionary learning for integrative, multimodal and scalable single-cell analysis

Humans monitor learning progress in curiosity-driven exploration

Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learning

Toward a unified framework for interpreting machine-learning models in neuroimaging

iBEAT V2.0: a multisite-applicable, deep learning-based pipeline for infant cerebral cortical surface reconstruction

The touchscreen operant platform for testing learning and memory in rats and mice

Deep learning-assisted comparative analysis of animal trajectories with DeepHL

A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei

A versatile active learning workflow for optimization of genetic and metabolic networks

Clearing Opacity Through Machine Learning

Deep learning the structural determinants of protein biochemical properties by comparing structural ensembles with DiffNets

Machine Learning as Natural Monopoly

Deep learning powers a motion-tracking revolution

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