Predicting protein submitochondrial locations by incorporating the pseudo-position specific scoring matrix into the general Chou's pseudo-amino acid composition

Qiu, Wenying; Li, Shan; Cui, Xiaowen; Yu, Zhaomin; Wang, Minghui; Du, Junwei; Peng, Yanjun; Yu, Bin

doi:10.1016/j.jtbi.2018.04.026

Cited by 71 publications

(17 citation statements)

References 90 publications

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“…The process of wavelet denoising includes the following three parts: wavelet transform, processing of wavelet coefficients and wavelet inverse transform [49]. There are three commonly used methods of wavelet denoising: wavelet threshold denoising, modulus maximum denoising and spatial correlation denoising.…”

Section: Methodsmentioning

confidence: 99%

Prediction of protein structural classes by different feature expressions based on 2-D wavelet denoising and fusion

Wang

2019

BMC Bioinformatics

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BackgroundProtein structural class predicting is a heavily researched subject in bioinformatics that plays a vital role in protein functional analysis, protein folding recognition, rational drug design and other related fields. However, when traditional feature expression methods are adopted, the features usually contain considerable redundant information, which leads to a very low recognition rate of protein structural classes.ResultsWe constructed a prediction model based on wavelet denoising using different feature expression methods. A new fusion idea, first fuse and then denoise, is proposed in this article. Two types of pseudo amino acid compositions are utilized to distill feature vectors. Then, a two-dimensional (2-D) wavelet denoising algorithm is used to remove the redundant information from two extracted feature vectors. The two feature vectors based on parallel 2-D wavelet denoising are fused, which is known as PWD-FU-PseAAC. The related source codes are available at https://github.com/Xiaoheng-Wang12/Wang-xiaoheng/tree/master.ConclusionsExperimental verification of three low-similarity datasets suggests that the proposed model achieves notably good results as regarding the prediction of protein structural classes.

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Section: Methodsmentioning

confidence: 99%

Prediction of protein structural classes by different feature expressions based on 2-D wavelet denoising and fusion

Wang

2019

BMC Bioinformatics

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“…Pseudo-amino acid composition (PAAC) and autocorrelation descriptor (Auto) are 145 utilized to extract the physicochemical and composition information. At present, PAAC has 146 shown good properties in proteomics field (Cui, et al, 2019;Qiu, et al, 2018;Yu, et al, 147 2017a; Yu, et al, 2017b;Yu, et al, 2017c). Auto includes 148 Moran, and Geary (Chen, Zhang, Ma & Yu, 2019;Chen, et al, 2018).…”

Section: Physicochemical Information 144mentioning

confidence: 99%

Prediction of protein-protein interactions based on elastic net and deep forest

Chen

et al. 2020

Preprint

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Prediction of protein-protein interactions (PPIs) helps to grasp molecular roots of 14 disease. However, web-lab experiments to predict PPIs are limited and costly. Using 15 machine-learning-based frameworks can not only automatically identify PPIs, but also 16 provide new ideas for drug research and development from a promising alternative. We 17 present a novel deep-forest-based method for PPIs prediction. First, pseudo amino acid 18 composition (PAAC), autocorrelation descriptor (Auto), multivariate mutual information 19 (MMI), composition-transition-distribution (CTD), and amino acid composition PSSM 20 (AAC-PSSM), and dipeptide composition PSSM (DPC-PSSM) are adopted to extract and 21 construct the pattern of PPIs. Secondly, elastic net is utilized to optimize the initial feature 22 vectors and boost the predictive performance. Finally, GcForest-PPI model based on deep 23 forest is built up. Benchmark experiments reveal that the accuracy values of Saccharomyces 24 cerevisiae and Helicobacter pylori are 95.44% and 89.26%. We also apply GcForest-PPI on 25 independent test sets and CD9-core network, crossover network, and cancer-specific network. 26The evaluation shows that GcForest-PPI can boost the prediction accuracy, complement 27 experiments and improve drug discovery. The datasets and code of GcForest-PPI could be 28 downloaded at https://github.com/QUST-AIBBDRC/GcForest-PPI/. 29

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“…Although PSSM P contains evolutionary information, it ignores the sequence order information. At present, PsePSSM [46] has been applied to human protein subcellular localization identification [47], protein submitochondrial localization [48], drug-target interaction prediction [49], membrane protein recognition [50] and etc. PsePSSM is shown in equation 7.…”

Section: Pseudo-position Specific Scoring Matrix (Psepssm)mentioning

confidence: 99%

StackPDB: predicting DNA-binding proteins based on XGB-RFE feature optimization and stacked ensemble classifier

Zhang

Liu

et al. 2020

Preprint

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DNA binding proteins (DBPs) not only play an important role in all aspects of genetic activities such as DNA replication, recombination, repair, and modification but also are used as key components of antibiotics, steroids, and anticancer drugs in the field of drug discovery. Identifying DBPs becomes one of the most challenging problems in the domain of proteomics research. Considering the high-priced and inefficient of the experimental method, constructing a detailed DBPs prediction model becomes an urgent problem for researchers. In this paper, we propose a stacked ensemble classifier based method for predicting DBPs called StackPDB. Firstly, pseudo amino acid composition (PseAAC), pseudo position-specific scoring matrix (PsePSSM), position-specific scoring matrix-transition probability composition (PSSM-TPC), evolutionary distance transformation (EDT), and residue probing transformation (RPT) are applied to extract protein sequence features. Secondly, extreme gradient boosting-recursive feature elimination (XGB-RFE) is employed to gain an excellent feature subset. Finally, the best features are applied to the stacked ensemble classifier composed of XGBoost, LightGBM, and SVM to construct StackPDB. After applying leave-one-out cross-validation (LOOCV), StackPDB obtains high ACC and MCC on PDB1075, 93.44% and 0.8687, respectively. Besides, the ACC of the independent test datasets PDB186 and PDB180 are 84.41% and 90.00%, respectively. The MCC of the independent test datasets PDB186 and PDB180 are 0.6882 and 0.7997, respectively. The results on the training dataset and the independent test dataset show that StackPDB has a great predictive ability to predict DBPs.

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Predicting protein submitochondrial locations by incorporating the pseudo-position specific scoring matrix into the general Chou's pseudo-amino acid composition

Cited by 71 publications

References 90 publications

Prediction of protein structural classes by different feature expressions based on 2-D wavelet denoising and fusion

Prediction of protein structural classes by different feature expressions based on 2-D wavelet denoising and fusion

Prediction of protein-protein interactions based on elastic net and deep forest

StackPDB: predicting DNA-binding proteins based on XGB-RFE feature optimization and stacked ensemble classifier

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