Identification of Secretory Proteins of Malaria Parasite by Feature Selection Technique

Tang, Hua; Zhang, Chunmei; Chen, Rong; Huang, Po Hsien; Duan, Chenggang; Ping, Zou

doi:10.2174/1570178614666170329155502

Cited by 17 publications

(5 citation statements)

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“…Identifying drug targets in the proteome of the malaria parasite is also a key step in the treatment of malaria. Scholars have designed various methods to identify mitochondrial proteins [35][36][37][38][39] and secreted proteins [40][41][42][43] of the malaria parasite.…”

Section: F I G U R E 1 Structures Of a Selection Of Known Antimalariamentioning

confidence: 99%

Multistage inhibitors of the malaria parasite: Emerging hope for chemoprotection and malaria eradication

Poonam

Gupta

et al. 2018

Medicinal Research Reviews

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Over time, several exciting advances have been made in the treatment and prevention of malaria; however, this devastating disease continues to be a major global health problem and affects millions of people every year. Notably, the paucity of new efficient drug molecules and the inevitable drug resistance of the malaria parasite, Plasmodium falciparum, against frontline therapeutics are the foremost struggles facing malaria eradication initiatives. According to the malaria eradication agenda, the discovery of new chemical entities that can destroy the parasite at the liver stage, the asexual blood stage, the gametocyte stage, and the insect ookinete stage of the parasite life cycle (i.e., compounds exhibiting multistage activity) are in high demand, preferably with novel and multiple modes of action. Phenotypic screening of chemical libraries against the malaria parasite is certainly a crucial step toward overcoming these crises. In the last few years, various research groups, including industrial research laboratories, have performed large-scale phenotypic screenings that have identified a wealth of chemical entities active against multiple life stages of the malaria parasite. Vital scientific and technological developments have led to the discovery of multistage inhibitors of the malaria parasite; these compounds, considered highly valuable starting points for subsequent drug discovery and eradication of malaria, are reviewed.

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Section: F I G U R E 1 Structures Of a Selection Of Known Antimalariamentioning

confidence: 99%

Multistage inhibitors of the malaria parasite: Emerging hope for chemoprotection and malaria eradication

Poonam

Gupta

et al. 2018

Medicinal Research Reviews

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“…Generally, user-friendly and publicly accessible web-servers (Lin et al, 2014 , 2017 ; Tang et al, 2016 , 2017 ; Yang et al, 2016 ; Chen et al, 2017 ; Li et al, 2017 ; Feng et al, 2018 ) or databases (He et al, 2015 ; Cui et al, 2017 ; Feng et al, 2017 ; Liang et al, 2017 ; Yi et al, 2017 ; Zhang T. et al, 2017 ) represent the future bioinformatics direction. Thus, for the convenience of fellow researchers, an online web server called IDAod is provided at http://bigroup.uestc.edu.cn/IDAod .…”

Section: Resultsmentioning

confidence: 99%

“…Feature reduction and learning are effective for precision improvement. They have been applied successfully in many bioinformatics problems (Zou et al, 2016 ; Tang et al, 2017 ; Wei et al, 2017 ). For the feature learning, we first built an autoencoder (AE) (Vincent et al, 2010 ) that the input layer followed by an encoder and decoder then connected to the output layer.…”

Section: Methodsmentioning

confidence: 99%

Identification of Antioxidant Proteins With Deep Learning From Sequence Information

et al. 2018

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Antioxidant proteins have been found closely linked to disease control for its ability to eliminate excess free radicals. Because of its medicinal value, the study of identifying antioxidant proteins is on the upsurge. Many machine-learning classifiers have performed poorly owing to the nonlinear and unbalanced nature of biological data. Recently, deep learning techniques showed advantages over many state-of-the-art machine learning methods in various fields. In this study, a deep learning based classifier was proposed to identify antioxidant proteins based on mixed g-gap dipeptide composition feature vector. The classifier employed deep autoencoder to extract nonlinear representation from raw input. The t-Distributed Stochastic Neighbor Embedding (t-SNE) was used for dimensionality reduction. Support vector machine was finally performed for classification. The classifier achieved F1 score of 0.8842 and MCC of 0.7409 in 10-fold cross validation. Experimental results show that our proposed method outperformed the traditional machine learning methods and could be a promising tool for antioxidant protein identification. For the convenience of others' scientific research, we have developed a user-friendly web server called IDAod for antioxidant protein identification, which can be accessed freely at http://bigroup.uestc.edu.cn/IDAod/.

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“…The combined features could also cause a lot of inconvenience, such as noise, dimension disaster, and so on. Analysis of variance (Feng et al, 2013;Tang et al, 2017;Xianfang et al, 2019), principal component analysis (Dong et al, 2015), minimal redundancy maximal relevance (Ding et al, 2013), maximum relevance maximum distance (Zou et al, 2016b), and increment of diversity (Zuo and Li, 2009;Zhao et al, 2010;Fan and Li, 2012) can solve these problems. In our study, ANOVA is used to screen the best feature set; the idea is to calculate the ratio of the categories to sample variance.…”

Section: Feature Selection Methodsmentioning

confidence: 99%

“…Obviously, features with larger ratios are more suitable for classification. The details can be referred from Feng et al (2013), Tang et al (2017) and Xianfang et al (2019).…”

Section: Feature Selection Methodsmentioning

confidence: 99%

Predicting Bacteriophage Enzymes and Hydrolases by Using Combined Features

Wang

Tang

2020

Front. Bioeng. Biotechnol.

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Bacteriophage is a type of virus that could infect the host bacteria. They have been applied in the treatment of pathogenic bacterial infection. Phage enzymes and hydrolases play the most important role in the destruction of bacterial cells. Correctly identifying the hydrolases coded by phage is not only beneficial to their function study, but also conducive to antibacteria drug discovery. Thus, this work aims to recognize the enzymes and hydrolases in phage. A combination of different features was used to represent samples of phage and hydrolase. A feature selection technique called analysis of variance was developed to optimize features. The classification was performed by using support vector machine (SVM). The prediction process includes two steps. The first step is to identify phage enzymes. The second step is to determine whether a phage enzyme is hydrolase or not. The jackknife cross-validated results showed that our method could produce overall accuracies of 85.1 and 94.3%, respectively, for the two predictions, demonstrating that the proposed method is promising.

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Identification of Secretory Proteins of Malaria Parasite by Feature Selection Technique

Cited by 17 publications

References 0 publications

Multistage inhibitors of the malaria parasite: Emerging hope for chemoprotection and malaria eradication

Multistage inhibitors of the malaria parasite: Emerging hope for chemoprotection and malaria eradication

Identification of Antioxidant Proteins With Deep Learning From Sequence Information

Predicting Bacteriophage Enzymes and Hydrolases by Using Combined Features

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