Allison N. Tegge

Molecular Biology · 2008–2009

A researcher in the Science corpus: 3 papers; trusts 34 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 59 citations into it; #4,797 of 17,773 by trust here, #8,905 by citations
• Materials Chemistry — 31 citations into it; #3,356 of 11,513 by trust here, #7,310 by citations
• Computational Biology & Bioinformatics — 25 citations into it; #399 of 660 by trust here, #539 by citations
• Computational Theory and Mathematics — 11 citations into it; #11,024 of 12,182 by trust here, #6,165 by citations
• Artificial Intelligence — 8 citations into it

Whom Allison N. Tegge trusts overall

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

1. Yang Zhang
2. Pierre Baldi
3. Gianluca Pollastri
4. Jianlin Cheng
5. John Moult
6. Jeffrey Skolnick
7. Johannes Söding
8. Christopher K. I. Williams
9. Burkhard Rost
10. Sean R. Eddy

Who trusts Allison N. Tegge overall

1. Robin Pearce
2. Badri Adhikari
3. Mirko Torrisi
4. Sitao Wu
5. Yang Zhang
6. Jianlin Cheng
7. Zhiyong Wang
8. Jianzhu Ma
9. Renzhi Cao
10. Debswapna Bhattacharya

Papers from Allison N. Tegge's perspective

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

1. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.
2. The Protein Data Bank.
3. Hidden Markov models for detecting remote protein homologies
4. Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions.
5. Prediction of protein secondary structure at better than 70% accuracy.
6. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
7. Hidden Markov models in computational biology. Applications to protein modeling.
8. Recovery of protein structure from contact maps.
9. Attention is All you Need (Neural Information Processing Systems)
10. Distance‐scaled, finite ideal‐gas reference state improves structure‐derived potentials of mean force for structure selection and stability prediction

Their papers

• NNcon: improved protein contact map prediction using 2D-recursive neural networks (2009) — 164 citations
• Evaluating the absolute quality of a single protein model using structural features and support vector machines (2009) — 103 citations
• Machine Learning Methods for Protein Structure Prediction (2008) — 127 citations