C. Bailey‐Kellogg

Molecular Biology · 2005–2021

A researcher in the Science corpus: 7 papers; trusts 29 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 81 citations into it; #3,309 of 17,773 by trust here, #11,608 by citations
• Materials Chemistry — 23 citations into it; #7,259 of 11,513 by trust here, #10,845 by citations
• Computational Biology & Bioinformatics — 16 citations into it; #248 of 660 by trust here, #546 by citations
• Computational Theory and Mathematics — 12 citations into it
• Probabilistic & Bayesian Methods — 10 citations into it; #846 of 1,525 by trust here, #904 by citations

Whom C. Bailey‐Kellogg trusts overall

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

1. S. W. Lockless
2. John Thomas
3. Anthony Fodor
4. Aleksey A. Porollo
5. Aharon Armon
6. Rachel Karchin
7. Richard Durbin
8. Nir Friedman
9. Naren Ramakrishnan
10. Ying Huang

Who trusts C. Bailey‐Kellogg overall

1. Sivaraman Balakrishnan
2. Hetunandan Kamisetty
3. Jianzhu Ma
4. Hannes Stärk
5. John Thomas
6. Susann Vorberg
7. Sheng Wang
8. Octavian-Eugen Ganea

Papers from C. Bailey‐Kellogg's perspective

Ordered by how strongly the researchers C. Bailey‐Kellogg trusts endorse each paper. Same corpus, a different order for every researcher.

1. Information Theory, Inference, and Learning Algorithms
2. Influence of conservation on calculations of amino acid covariance in multiple sequence alignments
3. ImageNet classification with deep convolutional neural networks
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. Correlated mutations and residue contacts in proteins
6. Gradient-based learning applied to document recognition
7. De novo protein design: fully automated sequence selection.
8. Semi-Supervised Classification with Graph Convolutional Networks (International Conference on Learning Representations)
9. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
10. Attention is All you Need (Neural Information Processing Systems)

Their papers

• Protein Design by Sampling an Undirected Graphical Model of Residue Constraints (2009) — 31 citations
• Graphical models of protein–protein interaction specificity from correlated mutations and interaction data (2009) — 34 citations
• Protein interaction interface region prediction by geometric deep learning (2021) — 109 citations
• Graphical Models of Residue Coupling in Protein Families (2008) — 15 citations
• DisruPPI: structure-based computational redesign algorithm for protein binding disruption (2018) — 15 citations
• MODELING AND INFERENCE OF SEQUENCE-STRUCTURE SPECIFICITY (2009) — 8 citations
• Graphical models of residue coupling in protein families (2005) — 76 citations