ArticleFrontiers in genetics2017
ir-HSP: Improved Recognition of Heat Shock Proteins, Their Families and Sub-types Based On
Article in Frontiers in genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 24 citations in OpenAlex.
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- ASRmiRNA: Abiotic Stress-Responsive miRNA Prediction in Plants by Using Machine Learning Algorithms with PseudoInternational journal of molecular sciences · 2022Article
- nhKcr: a new bioinformatics tool for predicting crotonylation sites on human nonhistone proteins based on deep learning.Briefings in bioinformatics · 2021Article
- In silico Design of a Vaccine Candidate for SAR S-CoV-2 Based on Multiple T-cell and B-cell Epitopes.Archives of Razi Institute · 2021Article
- mLoc-mRNA: predicting multiple sub-cellular localization of mRNAs using random forest algorithm coupled with feature selection via elastic net.BMC bioinformatics · 2021Article
- Signs of carcinogenicity induced by parathion, malathion, and estrogen in human breast epithelial cells (Review).Oncology reports · 2021Article
- Article
- Identifying Heat Shock Protein Families from Imbalanced Data by Using Combined Features.Computational and mathematical methods in medicine · 2020Article
- Identification of infectious disease-associated host genes using machine learning techniques.BMC bioinformatics · 2019Article
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- Identification of AtHsp90.6 involved in early embryogenesis and its structure prediction by molecular dynamics simulations.Royal Society open science · 2019Article
- Antimicrobial peptide similarity and classification through rough set theory using physicochemical boundaries.BMC bioinformatics · 2018Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Heat shock proteins (HSPs) play a pivotal role in cell growth and variability. Since conventional approaches are expensive and voluminous protein sequence information is available in the post-genomic era, development of an automated and accurate computational tool is highly desirable for prediction of HSPs, their families and sub-types. Thus, we propose a computational approach for reliable prediction of all these components in a single framework and with higher accuracy as well. The proposed approach achieved an overall accuracy of ~84% in predicting HSPs, ~97% in predicting six different families of HSPs, and ~94% in predicting four types of DnaJ proteins, with bench mark datasets. The developed approach also achieved higher accuracy as compared to most of the existing approaches. For easy prediction of HSPs by experimental scientists, a user friendly web server ir-HSP is made freely accessible at http://cabgrid.res.in:8080/ir-hsp. The ir-HSP was further evaluated for proteome-wide identification of HSPs by using proteome datasets of eight different species, and ~50% of the predicted HSPs in each species were found to be annotated with InterPro HSP families/domains. Thus, the developed computational method is expected to supplement the currently available approaches for prediction of HSPs, to the extent of their families and sub-types.
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