Navigating the pitfalls of applying machine learning in genomics

Systematic auditing is essential to debiasing machine learning in biology

(2021) Fatma-Elzahraa Eid et al.   Communications Biology

Veridical data science

(2020) Bin Yu et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

On instabilities of deep learning in image reconstruction and the potential costs of AI

(2020) Vegard Antun et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis

(2020) Agostina J. Larrazabal et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Machine learning and clinical epigenetics: a review of challenges for diagnosis and classification

(2020) S. Rauschert et al.   Clinical Epigenetics

Identifying CpG methylation signature as a promising biomarker for recurrence and immunotherapy in non–small-cell lung carcinoma

(2020) Ruihan Luo et al.   Aging-US

RaptRanker: in silico RNA aptamer selection from HT-SELEX experiment based on local sequence and structure information

(2020) Ryoga Ishida et al.   NUCLEIC ACIDS RESEARCH

Adversarial deconfounding autoencoder for learning robust gene expression embeddings

(2020) Ayse B Dincer et al.   BIOINFORMATICS

Fast and flexible linear mixed models for genome-wide genetics

(2019) Daniel E. Runcie et al.   PLoS Genetics

Deep Learning/Artificial Intelligence and Blood-Based DNA Epigenomic Prediction of Cerebral Palsy

(2019) Ray O. Bahado-Singh et al.   INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES

Deep learning: new computational modelling techniques for genomics

(2019) Gökcen Eraslan et al.   NATURE REVIEWS GENETICS

Comprehensive Integration of Single-Cell Data

(2019) Tim Stuart et al.   CELL

Inflated performance measures in enhancer–promoter interaction-prediction methods

(2019) Fan Cao et al.   NATURE GENETICS

Reply to ‘Inflated performance measures in enhancer–promoter interaction-prediction methods’

(2019) Sean Whalen et al.   NATURE GENETICS

Estimation of a significance threshold for genome-wide association studies

(2019) Avjinder S. Kaler et al.   BMC GENOMICS

Ethics of Artificial Intelligence in Radiology: Summary of the Joint European and North American Multisociety Statement

(2019) J. Raymond Geis et al.   RADIOLOGY

Valid Post-clustering Differential Analysis for Single-Cell RNA-Seq

(2019) Jesse M. Zhang et al.   Cell Systems

A resource-efficient tool for mixed model association analysis of large-scale data

(2019) Longda Jiang et al.   NATURE GENETICS

Establishment of Best Practices for Evidence for Prediction

(2019) Russell A. Poldrack et al.   JAMA Psychiatry

A Review of Domain Adaptation without Target Labels

(2019) Wouter M. Kouw et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

Epigenetic machine learning: utilizing DNA methylation patterns to predict spastic cerebral palsy

(2018) Erin L. Crowgey et al.   BMC BIOINFORMATICS

Opportunities and obstacles for deep learning in biology and medicine

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

DNA methylation-based classification of central nervous system tumours

(2018) David Capper et al.   NATURE

Integrating single-cell transcriptomic data across different conditions, technologies, and species

(2018) Andrew Butler et al.   NATURE BIOTECHNOLOGY

Accurate and sensitive quantification of protein-DNA binding affinity

(2018) Chaitanya Rastogi et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Epigenome-wide analysis in newborn blood spots from monozygotic twins discordant for cerebral palsy reveals consistent regional differences in DNA methylation

(2018) Namitha Mohandas et al.   Clinical Epigenetics

Analysis of 182 cerebral palsy transcriptomes points to dysregulation of trophic signalling pathways and overlap with autism

(2018) Clare L. van Eyk et al.   Translational Psychiatry

Genomic DNA Methylation-Derived Algorithm Enables Accurate Detection of Malignant Prostate Tissues

(2018) Erfan Aref-Eshghi et al.   Frontiers in Oncology

A systematic study of the class imbalance problem in convolutional neural networks

(2018) Mateusz Buda et al.   NEURAL NETWORKS

The Unreasonable Effectiveness of Convolutional Neural Networks in Population Genetic Inference

(2018) Lex Flagel et al.   MOLECULAR BIOLOGY AND EVOLUTION

A primer on deep learning in genomics

(2018) James Zou et al.   NATURE GENETICS

Avoiding common pitfalls in machine learning omic data science

(2018) Andrew E. Teschendorff   NATURE MATERIALS

DNA methylation-based forensic age prediction using artificial neural networks and next generation sequencing

(2017) Athina Vidaki et al.   Forensic Science International-Genetics

Deep Learning Accurately Predicts Estrogen Receptor Status in Breast Cancer Metabolomics Data

(2017) Fadhl M. Alakwaa et al.   JOURNAL OF PROTEOME RESEARCH

Imbalance-Aware Machine Learning for Predicting Rare and Common Disease-Associated Non-Coding Variants

(2017) Max Schubach et al.   Scientific Reports

Controlling the Rate of GWAS False Discoveries

(2016) Damian Brzyski et al.   GENETICS

A Comparison Study for DNA Motif Modeling on Protein Binding Microarray

(2016) Ka-Chun Wong et al.   IEEE-ACM Transactions on Computational Biology and Bioinformatics

High performance computation of landscape genomic models including local indicators of spatial association

(2016) S. Stucki et al.   Molecular Ecology Resources

Enhancer–promoter interactions are encoded by complex genomic signatures on looping chromatin

(2016) Sean Whalen et al.   NATURE GENETICS

Controlling the Rate of GWAS False Discoveries

(2016) Damian Brzyski et al.   GENETICS

Drug–target interaction prediction: databases, web servers and computational models

(2015) Xing Chen et al.   BRIEFINGS IN BIOINFORMATICS

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

(2015) Babak Alipanahi et al.   NATURE BIOTECHNOLOGY

Building accurate sequence-to-affinity models from high-throughput in vitro protein-DNA binding data using FeatureREDUCE

(2015) Todd R Riley et al.   eLife

Confounding and Heterogeneity in Genetic Association Studies with Admixed Populations

(2013) Jinghua Liu et al.   AMERICAN JOURNAL OF EPIDEMIOLOGY

On the definition of a confounder

(2013) Tyler J. VanderWeele et al.   ANNALS OF STATISTICS

Review of processing and analysis methods for DNA methylation array data

(2013) C S Wilhelm-Benartzi et al.   BRITISH JOURNAL OF CANCER

Response to ‘Predicting the diagnosis of autism spectrum disorder using gene pathway analysis’

(2013) E B Robinson et al.   MOLECULAR PSYCHIATRY

Population structure confounds autism genetic classifier

(2013) T G Belgard et al.   MOLECULAR PSYCHIATRY

Evaluation of methods for modeling transcription factor sequence specificity

(2013) Matthew T Weirauch et al.   NATURE BIOTECHNOLOGY

Predicting the diagnosis of autism spectrum disorder using gene pathway analysis

(2012) E Skafidas et al.   MOLECULAR PSYCHIATRY

A mixed-model approach for genome-wide association studies of correlated traits in structured populations

(2012) Arthur Korte et al.   NATURE GENETICS

Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses

(2012) Oliver Stegle et al.   Nature Protocols

The nature of confounding in genome-wide association studies

(2012) Bjarni J. Vilhjálmsson et al.   NATURE REVIEWS GENETICS

FIMO: scanning for occurrences of a given motif

(2011) Charles E. Grant et al.   BIOINFORMATICS

Predicting disease risks from highly imbalanced data using random forest

(2011) Mohammed Khalilia et al.   BMC Medical Informatics and Decision Making

Direct measurement of DNA affinity landscapes on a high-throughput sequencing instrument

(2011) Razvan Nutiu et al.   NATURE BIOTECHNOLOGY

A Linear Model for Transcription Factor Binding Affinity Prediction in Protein Binding Microarrays

(2011) Matti Annala et al.   PLoS One

Confounding Control in Healthcare Database Research

(2010) M. Alan Brookhart et al.   MEDICAL CARE

Tackling the widespread and critical impact of batch effects in high-throughput data

(2010) Jeffrey T. Leek et al.   NATURE REVIEWS GENETICS

Correction for hidden confounders in the genetic analysis of gene expression

(2010) J. Listgarten et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

High Resolution Models of Transcription Factor-DNA Affinities Improve In Vitro and In Vivo Binding Predictions

(2010) Phaedra Agius et al.   PLoS Computational Biology

Papers on normalization, variable selection, classification or clustering of microarray data

(2009) D. M. Rocke et al.   BIOINFORMATICS

A Survey on Transfer Learning

(2009) Sinno Jialin Pan et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

Repeatability and Reproducibility in Proteomic Identifications by Liquid Chromatography−Tandem Mass Spectrometry

(2009) David L. Tabb et al.   JOURNAL OF PROTEOME RESEARCH

Universal protein-binding microarrays for the comprehensive characterization of the DNA-binding specificities of transcription factors

(2009) Michael F Berger et al.   Nature Protocols

A critical assessment of Mus musculus gene function prediction using integrated genomic evidence

(2008) Lourdes Peña-Castillo et al.   GENOME BIOLOGY