Hetunandan Kamisetty

Molecular Biology · 2009–2017

A researcher in the Science corpus: 9 papers; trusts 60 researchers and is trusted by 112 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 205 citations into it; #179 of 17,773 by trust here, #971 by citations
• Computational Biology & Bioinformatics — 100 citations into it; #35 of 660 by trust here, #78 by citations
• Materials Chemistry — 68 citations into it; #293 of 11,513 by trust here, #2,285 by citations
• Spectroscopy — 27 citations into it
• Genetics — 25 citations into it; #1,034 of 6,854 by trust here, #1,251 by citations
• Probabilistic & Bayesian Methods — 13 citations into it
• Computational Theory and Mathematics — 8 citations into it
• Atomic and Molecular Physics, and Optics — 6 citations into it

Whom Hetunandan Kamisetty trusts overall

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

1. John Thomas
2. Johannes Söding
3. Yang Zhang
4. Sean R. Eddy
5. David T. Jones
6. Guoli Wang
7. David Baker
8. Naren Ramakrishnan
9. Sivaraman Balakrishnan
10. Jeffrey Skolnick

Who trusts Hetunandan Kamisetty overall

1. Sergey Ovchinnikov
2. Sheng Wang
3. Jinbo Xu
4. Stefan Seemayer
5. H. Szurmant
6. Ivan V. Anishchenko
7. David Baker
8. Roshan Rao
9. Duccio Malinverni
10. Martin Weigt

Papers from Hetunandan Kamisetty's perspective

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

1. The Protein Data Bank.
2. Correlated mutations and residue contacts in proteins
3. Identification of direct residue contacts in protein–protein interaction by message passing
4. The Pfam protein families database
5. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Attention is All you Need (Neural Information Processing Systems)
8. Direct-coupling analysis of residue coevolution captures native contacts across many protein families
9. ImageNet classification with deep convolutional neural networks
10. HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment

Their papers

• Assessing the utility of coevolution-based residue–residue contact predictions in a sequence- and structure-rich era (2013) — 660 citations
• Protein Structure Determination using Metagenome sequence data (2017) — 478 citations
• Robust and accurate prediction of residue–residue interactions across protein interfaces using evolutionary information (2014) — 601 citations
• Learning generative models for protein fold families (2011) — 341 citations
• Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing (2012) — 387 citations
• Large-scale determination of previously unsolved protein structures using evolutionary information (2015) — 129 citations
• Origins of coevolution between residues distant in protein 3D structures (2017) — 177 citations
• Computation and Functional Studies Provide a Model for the Structure of the Zinc Transporter hZIP4* (2015) — 64 citations
• MODELING AND INFERENCE OF SEQUENCE-STRUCTURE SPECIFICITY (2009) — 8 citations