ArticleBMC bioinformatics2011
DoBo: Protein domain boundary prediction by integrating evolutionary signals and machine learning.
Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 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
29 citing papers in PubMed.
- Comparative Dynamics Enables Discovery of Embedded Bacterial Ferredoxin Domains in Large Redox Enzymes.Proteins · 2025Article
- Multi-head attention-based U-Nets for predicting protein domain boundaries using 1D sequence features and 2D distance maps.BMC bioinformatics · 2022Article
- Modeling coronavirus spike protein dynamics: implications for immunogenicity and immune escape.Biophysical journal · 2021Article
- High-Performance Deep Learning Toolbox for Genome-Scale Prediction of Protein Structure and Function.Workshop on Machine Learning in HPC Environments. Workshop on Machine Learning in HPC Environments · 2021Article
- Protein domain identification methods and online resources.Computational and structural biotechnology journal · 2021Review
- FUpred: detecting protein domains through deep-learning-based contact map prediction.Bioinformatics (Oxford, England) · 2020Article
- Modeling the Tertiary Structure of the Rift Valley Fever Virus L Protein.Molecules (Basel, Switzerland) · 2019Article
- Identification and localization of Tospovirus genus-wide conserved residues in 3D models of the nucleocapsid and the silencing suppressor proteins.Virology journal · 2019Article
- DeepDom: Predicting protein domain boundary from sequence alone using stacked bidirectional LSTM.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2019Article
- PVP-SVM: Sequence-Based Prediction of Phage Virion Proteins Using a Support Vector Machine.Frontiers in microbiology · 2018Article
- Functional regions of the peroxin Pex19 necessary for peroxisome biogenesis.The Journal of biological chemistry · 2017Article
- 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
- Accurate prediction of protein relative solvent accessibility using a balanced model.BioData mining · 2017Article
- Observation selection bias in contact prediction and its implications for structural bioinformatics.Scientific reports · 2016Article
- Roles of the novel coiled-coil protein Rng10 in septum formation during fission yeast cytokinesis.Molecular biology of the cell · 2016Article
- PAT: predictor for structured units and its application for the optimization of target molecules for the generation of synthetic antibodies.BMC bioinformatics · 2016Article
- PDP-CON: prediction of domain/linker residues in protein sequences using a consensus approach.Journal of molecular modeling · 2016Article
- Serum Albumin Domain Structures in Human Blood Serum by Mass Spectrometry and Computational Biology.Molecular & cellular proteomics : MCP · 2016Article
- 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
Abstract
backgroundAccurate identification of protein domain boundaries is useful for protein structure determination and prediction. However, predicting protein domain boundaries from a sequence is still very challenging and largely unsolved.
resultsWe developed a new method to integrate the classification power of machine learning with evolutionary signals embedded in protein families in order to improve protein domain boundary prediction. The method first extracts putative domain boundary signals from a multiple sequence alignment between a query sequence and its homologs. The putative sites are then classified and scored by support vector machines in conjunction with input features such as sequence profiles, secondary structures, solvent accessibilities around the sites and their positions. The method was evaluated on a domain benchmark by 10-fold cross-validation and 60% of true domain boundaries can be recalled at a precision of 60%. The trade-off between the precision and recall can be adjusted according to specific needs by using different decision thresholds on the domain boundary scores assigned by the support vector machines.
conclusionsThe good prediction accuracy and the flexibility of selecting domain boundary sites at different precision and recall values make our method a useful tool for protein structure determination and modelling. The method is available at http://sysbio.rnet.missouri.edu/dobo/.
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.