Søren Buus

Molecular Biology · 2003–2018

A researcher in the Science corpus: 11 papers; trusts 9 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 36 citations into it; #3,959 of 17,773 by trust here, #3,891 by citations
• Immunology — 17 citations into it; #561 of 2,160 by trust here, #719 by citations
• Computational Biology & Bioinformatics — 6 citations into it

Whom Søren Buus trusts overall

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

1. Burkhard Rost
2. Morten Nielsen
3. Christopher Sander
4. Claus Lundegaard
5. Pedro A. Reche

Who trusts Søren Buus overall

1. J. L. Sánchez-Trincado
2. Morten Nielsen
3. Massimo Andreatta
4. Marta Gomez-Perosanz
5. Ward Fleri

Papers from Søren Buus's perspective

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

1. Amino acid substitution matrices from protein blocks.
2. Selection of representative protein data sets
3. A Community Resource Benchmarking Predictions of Peptide Binding to MHC-I Molecules (PLoS Comput. Biol.)
4. Prediction of MHC class I binding peptides using profile motifs. (Human Immunology)
5. Profile hidden Markov models
6. The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003
7. Reliable prediction of T‐cell epitopes using neural networks with novel sequence representations
8. NetMHCpan, a Method for Quantitative Predictions of Peptide Binding to Any HLA-A and -B Locus Protein of Known Sequence
9. The Immune Epitope Database and Analysis Resource: From Vision to Blueprint (PLoS Biology)
10. An integrative approach to CTL epitope prediction: A combined algorithm integrating MHC class I binding, TAP transport efficiency, and proteasomal cleavage predictions (European Journal of Immunology)

Their papers

• A Community Resource Benchmarking Predictions of Peptide Binding to MHC-I Molecules (2006) — 308 citations
• NetMHCpan, a Method for Quantitative Predictions of Peptide Binding to Any HLA-A and -B Locus Protein of Known Sequence (2007) — 693 citations
• Improved methods for predicting peptide binding affinity to MHC class II molecules (2018) — 725 citations
• NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8–11 (2008) — 815 citations
• An integrative approach to CTL epitope prediction: A combined algorithm integrating MHC class I binding, TAP transport efficiency, and proteasomal cleavage predictions (2005) — 324 citations
• Reliable prediction of T‐cell epitopes using neural networks with novel sequence representations (2003) — 380 citations
• Immune epitope database analysis resource (IEDB-AR) (2008) — 391 citations
• Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction (2007) — 925 citations
• Sensitive quantitative predictions of peptide-MHC binding by a 'Query by Committee' artificial neural network approach. (2003) — 363 citations
• ArrayPitope: Automated Analysis of Amino Acid Substitutions for Peptide Microarray-Based Antibody Epitope Mapping (2017) — 16 citations
• NetMHCpan, a method for MHC class I binding prediction beyond humans (2008) — 807 citations