Eshel Faraggi

Molecular Biology · 2009–2014

A researcher in the Science corpus: 6 papers; trusts 21 researchers and is trusted by 30 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 102 citations into it; #1,049 of 17,773 by trust here, #4,000 by citations
• Materials Chemistry — 61 citations into it; #697 of 11,513 by trust here, #4,181 by citations
• Computational Biology & Bioinformatics — 25 citations into it
• Computational Theory and Mathematics — 9 citations into it; #5,159 of 12,182 by trust here, #2,689 by citations
• Spectroscopy — 8 citations into it
• Computational Chemistry & Drug Discovery — 5 citations into it

Whom Eshel Faraggi trusts overall

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

1. David E. Rumelhart
2. Hongyi Zhou
3. Yang Zhang
4. Geoffrey E. Hinton
5. W. Kabsch
6. Burkhard Rost
7. G. Fiquet
8. Vladimir N Uversky
9. Yaoqi Zhou
10. Temple F. Smith

Who trusts Eshel Faraggi overall

1. Sheng Wang
2. Yuedong Yang
3. Zhixiu Li
4. Jinbo Xu
5. Jack Hanson
6. Ke Chen
7. Sergey Ovchinnikov
8. Nawar Malhis
9. Yaoqi Zhou
10. Lukasz Kurgan

Papers from Eshel Faraggi's perspective

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

1. This PDF file includes: Materials and Methods
2. Learning representations by back-propagating errors
3. Neural Networks for Pattern Recognition
4. Identification of common molecular subsequences.
5. Twilight zone of protein sequence alignments.
6. Intrinsically unstructured proteins. (TIBS -Trends in Biochemical Sciences. Regular ed)
7. Attention is All you Need (Neural Information Processing Systems)
8. TM-align: a protein structure alignment algorithm based on the TM-score
9. ImageNet classification with deep convolutional neural networks
10. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)

Their papers

• Improving protein fold recognition and template-based modeling by employing probabilistic-based matching between predicted one-dimensional structural properties of query and corresponding native properties of templates (2011) — 321 citations
• Improving the prediction accuracy of residue solvent accessibility and real‐value backbone torsion angles of proteins by guided‐learning through a two‐layer neural network (2009) — 135 citations
• SPINE X: Improving protein secondary structure prediction by multi-step learning coupled with prediction of solvent accessible surface area and backbone torsion angles (2011) — 258 citations
• Intrinsically Semi-disordered State and Its Role in Induced Folding and Protein Aggregation (2013) — 62 citations
• Direct prediction of profiles of sequences compatible with a protein structure by neural networks with fragment‐based local and energy‐based nonlocal profiles (2014) — 84 citations
• SPINE-D: Accurate Prediction of Short and Long Disordered Regions by a Single Neural-Network Based Method (2012) — 172 citations