ArticleBMC bioinformatics2007
Support Vector Machine-based method for predicting subcellular localization of mycobacterial proteins using evolutionary information and motifs.
Article in BMC bioinformatics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 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
49 citing papers in PubMed, 138 citations in OpenAlex.
- Screening and in silico evaluation of candidate late-stage antigens for incorporation into multi-stage and post-exposure vaccines against tuberculosis.NPJ vaccines · 2026Review
- DeePhafier: a phage lifestyle classifier using a multilayer self-attention neural network combining protein information.Briefings in bioinformatics · 2024Article
- MERITS: a web-based integratedBioinformatics advances · 2024Article
- Mechanistic Insight into the Enzymatic Inhibition of β-Amyrin against Mycobacterial Rv1636: In Silico and In Vitro Approaches.Biology · 2022Article
- Potential Efficacy ofMolecules (Basel, Switzerland) · 2022Article
- Identification of genotypic variants and its proteomic mutations of Brazilian SARS-CoV-2 isolates.Virus research · 2022Article
- An Update on "Reverse Vaccinology": The Pathway from Genomes and Epitope Predictions to Tailored, Recombinant Vaccines.Methods in molecular biology (Clifton, N.J.) · 2022Review
- Dataset of complete genome assembly and analysis of mycobacterium tuberculosis strain SIT745/EAI1-MYS.Data in brief · 2020Article
- Recognition of Mitochondrial Proteins in Plasmodium Based on the Tripeptide Composition.Frontiers in cell and developmental biology · 2020Article
- Specific Binding Peptides from Rv3632: A Strategy for BlockingBioMed research international · 2019Article
- Integrated proteomics, genomics, metabolomics approaches reveal oxalic acid as pathogenicity factor in Tilletia indica inciting Karnal bunt disease of wheat.Scientific reports · 2018Article
- Identifying and characterising PPE7 (Rv0354c) high activity binding peptides and their role in inhibiting cell invasion.Molecular and cellular biochemistry · 2017Article
- Cytosolic Proteome Profiling of Aminoglycosides ResistantFrontiers in microbiology · 2016Article
- Structure-based functional annotation of hypothetical proteins from Candida dubliniensis: a quest for potential drug targets.3 Biotech · 2015Article
- nDNA-Prot: identification of DNA-binding proteins based on unbalanced classification.BMC bioinformatics · 2014Article
- Identifying the subfamilies of voltage-gated potassium channels using feature selection technique.International journal of molecular sciences · 2014Article
- Protein fold recognition using geometric kernel data fusion.Bioinformatics (Oxford, England) · 2014Article
- Support vector machine (SVM) based multiclass prediction with basic statistical analysis of plasminogen activators.BMC research notes · 2014Article
- acACS: improving the prediction accuracy of protein subcellular locations and protein classification by incorporating the average chemical shifts composition.TheScientificWorldJournal · 2014Article
- A survey of computational intelligence techniques in protein function prediction.International journal of proteomics · 2014Review
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 at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn past number of methods have been developed for predicting subcellular location of eukaryotic, prokaryotic (Gram-negative and Gram-positive bacteria) and human proteins but no method has been developed for mycobacterial proteins which may represent repertoire of potent immunogens of this dreaded pathogen. In this study, attempt has been made to develop method for predicting subcellular location of mycobacterial proteins.
resultsThe models were trained and tested on 852 mycobacterial proteins and evaluated using five-fold cross-validation technique. First SVM (Support Vector Machine) model was developed using amino acid composition and overall accuracy of 82.51% was achieved with average accuracy (mean of class-wise accuracy) of 68.47%. In order to utilize evolutionary information, a SVM model was developed using PSSM (Position-Specific Scoring Matrix) profiles obtained from PSI-BLAST (Position-Specific Iterated BLAST) and overall accuracy achieved was of 86.62% with average accuracy of 73.71%. In addition, HMM (Hidden Markov Model), MEME/MAST (Multiple Em for Motif Elicitation/Motif Alignment and Search Tool) and hybrid model that combined two or more models were also developed. We achieved maximum overall accuracy of 86.8% with average accuracy of 89.00% using combination of PSSM based SVM model and MEME/MAST. Performance of our method was compared with that of the existing methods developed for predicting subcellular locations of Gram-positive bacterial proteins.
conclusionA highly accurate method has been developed for predicting subcellular location of mycobacterial proteins. This method also predicts very important class of proteins that is membrane-attached proteins. This method will be useful in annotating newly sequenced or hypothetical mycobacterial proteins. Based on above study, a freely accessible web server TBpred http://www.imtech.res.in/raghava/tbpred/ has been developed.
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.