ArticleThe protein journal2009
Improved prediction of protein binding sites from sequences using genetic algorithm.
Article in The protein journal, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- A Deep Learning and XGBoost-Based Method for Predicting Protein-Protein Interaction Sites.Frontiers in genetics · 2021Article
- SCRIBER: accurate and partner type-specific prediction of protein-binding residues from proteins sequences.Bioinformatics (Oxford, England) · 2019Article
- Sequence-based identification of interface residues by an integrative profile combining hydrophobic and evolutionary information.BMC bioinformatics · 2010Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We undertook this project in response to the rapidly increasing number of protein structures with unknown functions in the Protein Data Bank. Here, we combined a genetic algorithm with a support vector machine to predict protein-protein binding sites. In an experiment on a testing dataset, we predicted the binding sites for 66% of our datasets, made up of 50 testing hetero-complexes. This classifier achieved greater sensitivity (60.17%), specificity (58.17%), accuracy (64.08%), and F-measure (54.79%), and a higher correlation coefficient (0.2502) than those of the support vector machine. This result can be used to guide biologists in designing specific experiments for protein analysis.
Indexed as
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.