Casey S. Greene

Molecular Biology · 2009–2020

A researcher in the Science corpus: 18 papers; trusts 99 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 481 citations into it; #2,039 of 17,773 by trust here, #2,408 by citations
• Artificial Intelligence — 261 citations into it; #12,941 of 35,345 by trust here, #4,336 by citations
• Computer Vision and Pattern Recognition — 116 citations into it
• Genetics — 98 citations into it; #1,212 of 6,854 by trust here, #3,155 by citations
• Cancer Research — 91 citations into it; #956 of 1,780 by trust here, #1,010 by citations
• Computational Theory and Mathematics — 80 citations into it
• Computational Biology & Bioinformatics — 52 citations into it; #602 of 660 by trust here, #253 by citations
• Biophysics — 40 citations into it; #811 of 1,196 by trust here, #151 by citations

Whom Casey S. Greene trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Igor Kononenko
2. Geoffrey E. Hinton
3. Pascal Vincent
4. Yoshua Bengio
5. Gordon K. Smyth
6. Hugo Larochelle
7. Christian Szegedy
8. Marko Robnik-Sikonja
9. Diederik P. Kingma
10. Alex Krizhevsky

Who trusts Casey S. Greene overall

1. Brett K. Beaulieu-Jones
2. Ryan J. Urbanowicz
3. Jing Li
4. Sameer Quazi
5. G. Way
6. Gabriel Erion-Barner
7. Travers Ching
8. Daniel S. Himmelstein
9. Marylyn DeRiggi Ritchie
10. Jason H. Moore

Papers from Casey S. Greene's perspective

Ordered by how strongly the researchers Casey S. Greene trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet Large Scale Visual Recognition Challenge
8. Going deeper with convolutions
9. ImageNet: A large-scale hierarchical image database
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Opportunities and obstacles for deep learning in biology and medicine (2017) — 2,030 citations
• Understanding multicellular function and disease with human tissue-specific networks (2015) — 816 citations
• Genomic and Molecular Landscape of DNA Damage Repair Deficiency across The Cancer Genome Atlas (2018) — 959 citations
• Functional Knowledge Transfer for High-accuracy Prediction of Under-studied Biological Processes (2013) — 66 citations
• Semi-Supervised Learning of the Electronic Health Record for Phenotype Stratification (2016) — 171 citations
• ADAGE-Based Integration of Publicly Available Pseudomonas aeruginosa Gene Expression Data with Denoising Autoencoders Illuminates Microbe-Host Interactions (2016) — 108 citations
• Unsupervised Feature Construction and Knowledge Extraction from Genome-Wide Assays of Breast Cancer with Denoising Autoencoders (2014) — 143 citations
• The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens (2019) — 434 citations
• Extracting a biologically relevant latent space from cancer transcriptomes with variational autoencoders (2017) — 260 citations
• Spatially Uniform ReliefF (SURF) for computationally-efficient filtering of gene-gene interactions (2009) — 148 citations
• Transparency and reproducibility in artificial intelligence (2020) — 429 citations
• Enhancing rigor and reproducibility by improving software availability, usability, and archival stability (2020)
• Oncogenic Signaling Pathways in The Cancer Genome Atlas (2018) — 2,663 citations
• Privacy-Preserving Generative Deep Neural Networks Support Clinical Data Sharing (2017) — 468 citations
• Reproducibility of computational workflows is automated using continuous analysis (2017) — 157 citations
• Celebrating parasites (2017) — 37 citations
• Evaluating deep variational autoencoders trained on pan-cancer gene expression (2017) — 15 citations
• Unsupervised extraction of functional gene expression signatures in the bacterial pathogen Pseudomonas aeruginosa with eADAGE (2016) — 5 citations