Identifying Heat Shock Protein Families from Imbalanced Data by Using Combined Features
Joint Authors
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-09-23
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Jing, Xiao-Yang& Li, Feng-Min. 2020. Identifying Heat Shock Protein Families from Imbalanced Data by Using Combined Features. Computational and Mathematical Methods in Medicine،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1139648
Modern Language Association (MLA)
Jing, Xiao-Yang& Li, Feng-Min. Identifying Heat Shock Protein Families from Imbalanced Data by Using Combined Features. Computational and Mathematical Methods in Medicine No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1139648
American Medical Association (AMA)
Jing, Xiao-Yang& Li, Feng-Min. Identifying Heat Shock Protein Families from Imbalanced Data by Using Combined Features. Computational and Mathematical Methods in Medicine. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1139648
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1139648