S. Joshua Swamidass

Molecular Biology · 2005–2018

A researcher in the Science corpus: 11 papers; trusts 26 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 307 citations into it; #5,811 of 17,773 by trust here, #2,855 by citations
• Artificial Intelligence — 225 citations into it; #33,093 of 35,345 by trust here, #6,378 by citations
• Computational Theory and Mathematics — 123 citations into it; #4,788 of 12,182 by trust here, #872 by citations
• Computer Vision and Pattern Recognition — 96 citations into it
• Computational Chemistry & Drug Discovery — 54 citations into it; #288 of 425 by trust here, #258 by citations
• Materials Chemistry — 51 citations into it; #5,881 of 11,513 by trust here, #6,024 by citations
• Computational Biology & Bioinformatics — 31 citations into it
• Statistical Learning Theory — 22 citations into it

Whom S. Joshua Swamidass trusts overall

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

1. Christopher K. I. Williams
2. Pierre Baldi
3. David E. Heckerman
4. Judea Pearl
5. Tommi S. Jaakkola
6. Christina S. Leslie
7. David Haussler
8. Alessandro Sperduti
9. John C. Gower
10. Hisashi Kashima

Who trusts S. Joshua Swamidass overall

1. Kyle Swanson
2. Georg Hinselmann
3. Fabrizio Costa
4. Lars Rosenbaum
5. Pierre Mahé
6. Alessio Ceroni
7. Nikil Wale
8. Garrett B. Goh
9. Nils M. Kriege
10. Liva Ralaivola

Papers from S. Joshua Swamidass's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet Large Scale Visual Recognition Challenge
8. Learning Multiple Layers of Features from Tiny Images
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
• Kernels for small molecules and the prediction of mutagenicity, toxicity and anti-cancer activity (2005) — 206 citations
• Graph kernels for chemical informatics (2005) — 513 citations
• One- to Four-Dimensional Kernels for Virtual Screening and the Prediction of Physical, Chemical, and Biological Properties (2007) — 63 citations
• Modeling Reactivity to Biological Macromolecules with a Deep Multitask Network (2016) — 95 citations
• A survey of current trends in computational drug repositioning (2016) — 476 citations
• XenoSite: Accurately Predicting CYP-Mediated Sites of Metabolism with Neural Networks (2013) — 201 citations
• Learning a Local-Variable Model of Aromatic and Conjugated Systems (2018) — 26 citations
• Open Source Drug Discovery with the Malaria Box Compound Collection for Neglected Diseases and Beyond (2016) — 364 citations
• Accurate and efficient target prediction using a potency-sensitive influence-relevance voter (2015) — 9 citations
• The Influence Relevance Voter: An Accurate And Interpretable Virtual High Throughput Screening Method (2009) — 56 citations