Daniel B. Roche

Molecular Biology · 2010–2019

A researcher in the Science corpus: 12 papers; trusts 51 researchers and is trusted by 38 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 159 citations into it; #527 of 17,773 by trust here, #3,389 by citations
• Materials Chemistry — 80 citations into it; #427 of 11,513 by trust here, #4,389 by citations
• Computational Biology & Bioinformatics — 55 citations into it
• Computational Theory and Mathematics — 18 citations into it; #5,735 of 12,182 by trust here, #5,954 by citations
• Evaluation & Benchmarks — 8 citations into it
• Information Systems and Management — 5 citations into it

Whom Daniel B. Roche trusts overall

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

1. Liam J. McGuffin
2. Yang Zhang
3. Jeffrey Skolnick
4. Hongyi Zhou
5. Maria T. Buenavista
6. Arne Elofsson
7. Lawrence A. Kelley
8. Mindaugas Margelevičius
9. Krzysztof Ginalski
10. Domenico Cozzetto

Who trusts Daniel B. Roche overall

1. Liam J. McGuffin
2. Maria T. Buenavista
3. Ahmad N. Shuid
4. Jennifer D. Atkins
5. Ali Hassan Ahmed Maghrabi
6. G. Bunkoczi
7. Recep Adiyaman
8. Bajuna R. Salehe
9. Arne Elofsson
10. Jianlin Cheng

Papers from Daniel B. Roche's perspective

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

1. Basic local alignment search tool.
2. MaxSub: an automated measure for the assessment of protein structure prediction quality
3. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.
4. HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment
5. Comparative protein modelling by satisfaction of spatial restraints.
6. SCOP: a structural classification of proteins database for the investigation of sequences and structures.
7. Hidden Markov models for detecting remote protein homologies
8. The Protein Data Bank.
9. TM-align: a protein structure alignment algorithm based on the TM-score
10. Can correct protein models be identified?

Their papers

• The ModFOLD4 server for the quality assessment of 3D protein models (2013) — 120 citations
• Rapid model quality assessment for protein structure predictions using the comparison of multiple models without structural alignments (2010) — 107 citations
• Improvement of 3D protein models using multiple templates guided by single-template model quality assessment (2012) — 48 citations
• FunFOLD: an improved automated method for the prediction of ligand binding residues using 3D models of proteins (2011) — 69 citations
• The IntFOLD server: an integrated web resource for protein fold recognition, 3D model quality assessment, intrinsic disorder prediction, domain prediction and ligand binding site prediction (2011) — 107 citations
• IntFOLD: an integrated server for modelling protein structures and functions from amino acid sequences (2015) — 116 citations
• Automated tertiary structure prediction with accurate local model quality assessment using the intfold‐ts method (2011) — 35 citations
• DisProt 7.0: a major update of the database of disordered proteins (2016) — 138 citations
• The FunFOLD2 server for the prediction of protein–ligand interactions (2013) — 50 citations
• DisProt 7.0: a major update of the database of disordered proteins (2016) — 203 citations
• The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens (2019) — 434 citations
• Accurate template‐based modeling in CASP12 using the IntFOLD4‐TS, ModFOLD6, and ReFOLD methods (2018) — 53 citations