Casey S Greene

发表

ADAGE signature analysis: differential expression analysis with data-defined gene sets

Jie Tan, Matthew Huyck, Dongbo Hu, 2017, BMC Bioinformatics.

PSB 2019 Workshop on Text Mining and Visualization for Precision Medicine.

Jingcheng Du, Zhiyong Lu, Yong Chen, 2019, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

Accelerating epistasis analysis in human genetics with consumer graphics hardware

Fabio Cancare, Nicholas A. Sinnott-Armstrong, Casey S Greene, 2009, BMC Research Notes.

Embracing study heterogeneity for finding genetic interactions in large‐scale research consortia

Jing Huang, Elisabetta Manduchi, Casey S Greene, 2019, Genetic epidemiology.

Open collaborative writing with Manubot

Dongbo Hu, Casey S Greene, Anthony Gitter, 2019, PLoS Comput. Biol..

Parameter tuning is a key part of dimensionality reduction via deep variational autoencoders for single cell RNA transcriptomics

Casey S Greene, Qiwen Hu, C. Greene, 2018, bioRxiv.

Expanding and Remixing the Metadata Landscape

Casey S Greene, Ariel A Hippen, Ariel A. Hippen, 2020, Trends in cancer.

Correcting for experiment-specific variability in expression compendia can remove underlying signals

Deborah A Hogan, Casey S Greene, YoSon Park, 2020, GigaScience.

Transparency and reproducibility in artificial intelligence

Trevor Hastie, Joelle Pineau, Robert Tibshirani, 2020, Nature.

Big Data Bioinformatics

Jie Tan, Casey S Greene, Jason H Moore, 2014, Journal of cellular physiology.

Cross-platform normalization of microarray and RNA-seq data for machine learning applications

Jie Tan, Casey S Greene, Jeffrey A Thompson, 2016, PeerJ.

Unsupervised Extraction of Stable Expression Signatures from Public Compendia with an Ensemble of Neural Networks.

Jie Tan, Deborah A Hogan, Casey S Greene, 2017, Cell systems.

Accurate evaluation and analysis of functional genomics data and methods

Casey S Greene, Olga G Troyanskaya, O. Troyanskaya, 2012, Annals of the New York Academy of Sciences.

Targeted exploration and analysis of large cross-platform human transcriptomic compendia

Kai Li, Moses Charikar, Olga G. Troyanskaya, 2015, Nature Methods.

Bayesian deep learning for single-cell analysis

Casey S Greene, Gregory P Way, 2018, Nature Methods.

Sci-Hub provides access to nearly all scholarly literature

Casey S Greene, Stephen Reid McLaughlin, Daniel S. Himmelstein, 2018, eLife.

MultiPLIER: A Transfer Learning Framework for Transcriptomics Reveals Systemic Features of Rare Disease.

Casey S Greene, Matthias Kretzler, Qiwen Hu, 2019, Cell systems.

Biologically Informed Neural Networks Predict Drug Responses.

Casey S Greene, James C Costello, 2020, Cancer cell.

Reproducibility of computational workflows is automated using continuous analysis

Brett K. Beaulieu-Jones, Casey S Greene, Brett K Beaulieu-Jones, 2017, Nature Biotechnology.

An expanded evaluation of protein function prediction methods shows an improvement in accuracy

Tapio Salakoski, Paolo Fontana, Silvio C. E. Tosatto, 2016, Genome Biology.

A field guide to cultivating computational biology

Anne E Carpenter, Luigi Marchionni, Elana J Fertig, 2021, PLoS biology.

Reproducibility standards for machine learning in the life sciences

Su-In Lee, Casey S Greene, Michael M Hoffman, 2021, Nature Methods.

Predicting targeted drug combinations based on Pareto optimal patterns of coexpression network connectivity

Casey S Greene, Nadia M Penrod, Jason H Moore, 2013, Genome Medicine.

Integrative systems biology for data-driven knowledge discovery.

Casey S Greene, Olga G Troyanskaya, O. Troyanskaya, 2010, Seminars in nephrology.

New Drosophila Long-Term Memory Genes Revealed by Assessing Computational Function Prediction Methods

Predrag Radivojac, Casey S Greene, Iddo Friedberg, 2018, G3: Genes, Genomes, Genetics.

Celebrating parasites

Casey S Greene, Lana X Garmire, Marylyn D Ritchie, 2017, Nature Genetics.

Metabolic pathways and immunometabolism in rare kidney diseases

Casey S Greene, Matthias Kretzler, Peter A Merkel, 2018, Annals of the rheumatic diseases.

Data-Sharing Models

Casey S Greene, James Brian Byrd, C. Greene, 2017, The New England journal of medicine.

Ability of epistatic interactions of cytokine single-nucleotide polymorphisms to predict susceptibility to disease subsets in systemic sclerosis patients.

Casey S Greene, Lorenzo Beretta, Raffaella Scorza, 2008, Arthritis and rheumatism.

Text and data mining for biomedical discovery.

Jieping Ye, Udo Hahn, Kevin Bretonnel Cohen, 2013, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

Machine Learning Analysis Identifies Drosophila Grunge/Atrophin as an Important Learning and Memory Gene Required for Memory Retention and Social Learning

Casey S Greene, Giovanni Bosco, C. Greene, 2017, G3: Genes, Genomes, Genetics.

A parasite's perspective on data sharing

Casey S Greene, YoSon Park, C. Greene, 2018, GigaScience.

Comprehensive Cross-Population Analysis of High-Grade Serous Ovarian Cancer Supports No More Than Three Subtypes

Casey S Greene, Chen Wang, Ellen L Goode, 2016, G3: Genes, Genomes, Genetics.