ArticleComputational and mathematical methods in medicine2020
Identifying Heat Shock Protein Families from Imbalanced Data by Using Combined Features.
Article in Computational and mathematical methods in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.
- A Systematic Review of Proteomics in Obesity: Unpacking the Molecular Puzzle.Current obesity reports · 2024Pooled it
- Prediction of serum anti-HSP27 antibody titers changes using a light gradient boosting machine (LightGBM) technique.Scientific reports · 2023Article
- Expression Levels of Heat-Shock Proteins inInsects · 2023Article
- Linking Histone Methylation States andInsects · 2023Article
- General Structural and Functional Features of Molecular Chaperones.Advances in experimental medicine and biology · 2021Article
- Article
- Predicting Cell Wall Lytic Enzymes Using Combined Features.Frontiers in bioengineering and biotechnology · 2020Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Heat shock proteins (HSPs) are ubiquitous in living organisms. HSPs are an essential component for cell growth and survival; the main function of HSPs is controlling the folding and unfolding process of proteins. According to molecular function and mass, HSPs are categorized into six different families: HSP20 (small HSPS), HSP40 (J-proteins), HSP60, HSP70, HSP90, and HSP100. In this paper, improved methods for HSP prediction are proposed-the split amino acid composition (SAAC), the dipeptide composition (DC), the conjoint triad feature (CTF), and the pseudoaverage chemical shift (PseACS) were selected to predict the HSPs with a support vector machine (SVM). In order to overcome the imbalance data classification problems, the syntactic minority oversampling technique (SMOTE) was used to balance the dataset. The overall accuracy was 99.72% with a balanced dataset in the jackknife test by using the optimized combination feature SAAC+DC+CTF+PseACS, which was 4.81% higher than the imbalanced dataset with the same combination feature. The Sn, Sp, Acc, and MCC of HSP families in our predictive model were higher than those in existing methods. This improved method may be helpful for protein function prediction.
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Registered trials
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