Demo Data from EDBT 2026 / reviewers in the wild / expert
Patrik Björkholm
dblp:78/7064
· DBLP profile ↗
2ranked-venue papers
2as first author
0since 2021 · last 2009
0000-0002-9131-4122ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 2 · 2 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
2 papers |
Bioinformatics and computational biology · 100% |
Topics — the 6 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › protein-protein interaction prediction
domain-domain interaction prediction |
0.1 | 1 | 2009 | Comparative analysis and unification of domain-domain interaction networks · Bioinform. 2009 |
Bioinformatics and computational biology › protein analysis › protein-protein interaction
protein-protein interaction network |
0.1 | 1 | 2009 | Comparative analysis and unification of domain-domain interaction networks · Bioinform. 2009 |
Bioinformatics and computational biology
protein structure prediction |
0.1 | 1 | 2009 | Using multi-data hidden Markov models trained on local neighborhoods of protein structure to predict residue-residue contacts · Bioinform. 2009 |
Bioinformatics and computational biology › protein structure prediction
residue contact prediction |
0.1 | 1 | 2009 | Using multi-data hidden Markov models trained on local neighborhoods of protein structure to predict residue-residue contacts · Bioinform. 2009 |
Bioinformatics and computational biology › data integration
database integration |
0.0 | 1 | 2009 | Comparative analysis and unification of domain-domain interaction networks · Bioinform. 2009 |
Bioinformatics and computational biology › protein structure prediction
de novo structure prediction |
0.0 | 1 | 2009 | Using multi-data hidden Markov models trained on local neighborhoods of protein structure to predict residue-residue contacts · Bioinform. 2009 |
Methods — techniques the papers use, named apart from their topics
secondary structure prediction · 0.1reliability scoring · 0.1network comparison · 0.1local neighborhood descriptors · 0.1hidden markov model · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2009 | Using multi-data hidden Markov models trained on local neighborhoods of protein structure to predict residue-residue contactsabstractMOTIVATION: Correct prediction of residue-residue contacts in proteins that lack good templates with known structure would take ab initio protein structure prediction a large step forward. The lack of correct contacts, and in particular long-range contacts, is considered the main reason why these methods often fail. RESULTS: We propose a novel hidden Markov model (HMM)-based method for predicting residue-residue contacts from protein sequences using as training data homologous sequences, predicted secondary structure and a library of local neighborhoods (local descriptors of protein structure). The library consists of recurring structural entities incorporating short-, medium- and long-range interactions and is general enough to reassemble the cores of nearly all proteins in the PDB. The method is tested on an external test set of 606 domains with no significant sequence similarity to the training set as well as 151 domains with SCOP folds not present in the training set. Considering the top 0.2 x L predictions (L = sequence length), our HMMs obtained an accuracy of 22.8% for long-range interactions in new fold targets, and an average accuracy of 28.6% for long-, medium- and short-range contacts. This is a significant performance increase over currently available methods when comparing against results published in the literature. AVAILABILITY: http://predictioncenter.org/Services/FragHMMent/. Patrik Björkholm, Pawel Daniluk, Andriy Kryshtafovych, Krzysztof Fidelis, Robin Andersson, Torgeir R. Hvidsten |
Bioinform. | 1 |
| 2009 | Comparative analysis and unification of domain-domain interaction networksabstractMOTIVATION: Certain protein domains are known to preferentially interact with other domains. Several approaches have been proposed to predict domain-domain interactions, and over nine datasets are available. Our aim is to analyse the coverage and quality of the existing resources, as well as the extent of their overlap. With this knowledge, we have the opportunity to merge individual domain interaction networks to construct a comprehensive and reliable database. RESULTS: In this article we introduce a new approach towards comparing domain-domain interaction networks. This approach is used to compare nine predicted domain and protein interaction networks. The networks were used to generate a database of unified domain interactions, UniDomInt. Each interaction in the dataset is scored according to the benchmarked reliability of the sources. The performance of UniDomInt is an improvement compared to the underlying source networks and to another composite resource, Domine. AVAILABILITY: http://sonnhammer.sbc.su.se/download/UniDomInt/ Patrik Björkholm, Erik L. L. Sonnhammer |
Bioinform. | 1 |