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. 2020 Sep 23:2020:8894478.
doi: 10.1155/2020/8894478. eCollection 2020.

Identifying Heat Shock Protein Families from Imbalanced Data by Using Combined Features

Xiao-Yang Jing  1 , Feng-Min Li  1
Affiliations

Identifying Heat Shock Protein Families from Imbalanced Data by Using Combined Features

Xiao-Yang Jing et al. Comput Math Methods Med. .

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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Conflict of interest statement

The authors declare that there is no conflict of interest.

Figures

Figure 1
Figure 1
The flowchart of the proposed method. SAAC: split amino acid composition; DC: dipeptide composition; CTF: conjoint triad feature; PseACS: pseudoaverage chemical shift; SMOTE: syntactic minority oversampling technique.
Figure 2
Figure 2
Prediction results of different combined features. Numbers denote features: 1 for DC, 2 for CTF, 3 for PseACS, and 4 for SAAC.
Figure 3
Figure 3
The predictive sensitivity, specificity, MCC, and accuracy of HSPs by using four algorithms.
Figure 4
Figure 4
The predictive overall accuracy of HSPs by using four algorithms.
Figure 5
Figure 5
A comparison of the proposed method for independent datasets.
See this image and copyright information in PMC

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