ArticleNucleic acids research2009
FIEFDom: a transparent domain boundary recognition system using a fuzzy mean operator.
Article in Nucleic acids research, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
11 citing papers in PubMed.
- A unified approach to protein domain parsing with inter-residue distance matrix.Bioinformatics (Oxford, England) · 2023Article
- DeepDom: Predicting protein domain boundary from sequence alone using stacked bidirectional LSTM.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2019Article
- ThreaDomEx: a unified platform for predicting continuous and discontinuous protein domains by multiple-threading and segment assembly.Nucleic acids research · 2017Article
- Fast H-DROP: A thirty times accelerated version of H-DROP for interactive SVM-based prediction of helical domain linkers.Journal of computer-aided molecular design · 2017Article
- PDP-CON: prediction of domain/linker residues in protein sequences using a consensus approach.Journal of molecular modeling · 2016Article
- Article
- Protein inter-domain linker prediction using Random Forest and amino acid physiochemical properties.BMC bioinformatics · 2014Article
- ThreaDom: extracting protein domain boundary information from multiple threading alignments.Bioinformatics (Oxford, England) · 2013Article
- Structure prediction and analysis of DNA transposon and LINE retrotransposon proteins.The Journal of biological chemistry · 2013Article
- Article
- Article
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
Protein domain prediction is often the preliminary step in both experimental and computational protein research. Here we present a new method to predict the domain boundaries of a multidomain protein from its amino acid sequence using a fuzzy mean operator. Using the nr-sequence database together with a reference protein set (RPS) containing known domain boundaries, the operator is used to assign a likelihood value for each residue of the query sequence as belonging to a domain boundary. This procedure robustly identifies contiguous boundary regions. For a dataset with a maximum sequence identity of 30%, the average domain prediction accuracy of our method is 97% for one domain proteins and 58% for multidomain proteins. The presented model is capable of using new sequence/structure information without re-parameterization after each RPS update. When tested on a current database using a four year old RPS and on a database that contains different domain definitions than those used to train the models, our method consistently yielded the same accuracy while two other published methods did not. A comparison with other domain prediction methods used in the CASP7 competition indicates that our method performs better than existing sequence-based methods.
Indexed as
Identifiers
What OpenQuestion holds
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.