Improved prediction of protein–protein interactions using novel negative samples, features, and an ensemble classifier

Wei, Leyi; Xing, Pengwei; Zeng, Jiancang; Chen, JinXiu; Su, Ran; Guo, Fei

doi:10.1016/j.artmed.2017.03.001

Cited by 224 publications

(104 citation statements)

References 42 publications

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“…Chen & Liu, ; Ding et al., ; You et al., ), rotation forest (L. Wong et al., ), linear discriminant classifier and cloud points (Nanni, ), relaxed variable kernel density estimator (RVKDE; C.‐Y. Yu et al., ), an ensemble classifier (L. Wei et al., ), extreme learning machine (ELM; You et al., ), and k‐nearest neighbors (KNNs; Yang et al., ).…”

Section: Sequence‐based Methodsmentioning

confidence: 99%

Computational Methods for Predicting Protein‐Protein Interactions Using Various Protein Features

Ding

Kihara

2018

CP Protein Science

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Understanding protein-protein interactions (PPIs) in a cell is essential for learning protein functions, pathways, and mechanism of diseases. PPIs are also important targets for developing drugs. Experimental methods, both small-scale and large-scale, have identified PPIs in several model organisms. However, results cover only a part of PPIs of organisms; moreover, there are many organisms whose PPIs have not yet been investigated. To complement experimental methods, many computational methods have been developed that predict PPIs from various characteristics of proteins. Here we provide an overview of literature reports to classify computational PPI prediction methods that consider different features of proteins, including protein sequence, genomes, protein structure, function, PPI network topology, and those which integrate multiple methods. © 2018 by John Wiley & Sons, Inc.

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Section: Sequence‐based Methodsmentioning

confidence: 99%

Computational Methods for Predicting Protein‐Protein Interactions Using Various Protein Features

Ding

Kihara

2018

CP Protein Science

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“…ncbi.nlm.nih.gov/pubmed), and Gene Ontology (Ashburner et al, 2000) term annotation. The Human Protein Reference Database (HPRD) (Prasad et al, 2009) is a protein database for experimentally derived information about human proteomics, including protein and protein interactions (Ding et al, 2016;Wei et al, 2017a), post-translational modifications (PTMs) (Wei et al, 2017b) and other information. We download all human PPIs from this database, containing 15,231 proteins and 38,167 interactions.…”

Section: Analysis Of Disease Characteristics Of Hccmentioning

confidence: 99%

Predict New Therapeutic Drugs for Hepatocellular Carcinoma Based on Gene Mutation and Expression

Gao

2020

Front. Bioeng. Biotechnol.

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Hepatocellular carcinoma (HCC) is the fourth most common primary liver tumor and is an important medical problem worldwide. However, the use of current therapies for HCC is no possible to be cured, and despite numerous attempts and clinical trials, there are not so many approved targeted treatments for HCC. So, it is necessary to identify additional treatment strategies to prevent the growth of HCC tumors. We are looking for a systematic drug repositioning bioinformatics method to identify new drug candidates for the treatment of HCC, which considers not only aberrant genomic information, but also the changes of transcriptional landscapes. First, we screen the collection of HCC feature genes, i.e., kernel genes, which frequently mutated in most samples of HCC based on human mutation data. Then, the gene expression data of HCC in TCGA are combined to classify the kernel genes of HCC. Finally, the therapeutic score (TS) of each drug is calculated based on the kolmogorov-smirnov statistical method. Using this strategy, we identify five drugs that associated with HCC, including three drugs that could treat HCC and two drugs that might have side-effect on HCC. In addition, we also make Connectivity Map (CMap) profiles similarity analysis and KEGG enrichment analysis on drug targets. All these findings suggest that our approach is effective for accurate predicting novel therapeutic options for HCC and easily to be extended to other tumors.

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“…In our experiment, we used the following four indicators to evaluate the predictive performance of our proposed model, including Accuracy (ACC), Sensitivity (SN), Specificity (SP), and Mathew's Correlation Coefficient (MCC). They are the four commonly used indicators for classifier performance evaluation in other Bioinformatics fields (Zhang et al, 2008(Zhang et al, , 2018a(Zhang et al, ,b,c, 2019bWei et al, 2017bWei et al, , 2019bZeng et al, 2017bZeng et al, , 2019cChen et al, 2018;Lu et al, 2018a,b;Fu et al, 2019;Gong et al, 2019;Jin et al, 2019;Liu and Li, 2019;Liu et al, 2019c,d;Manavalan et al, 2019a,b,c,d;Basith et al, 2020). Their calculation formulas are as follows:…”

Section: Performance Indicatorsmentioning

confidence: 99%

Developing a Multi-Layer Deep Learning Based Predictive Model to Identify DNA N4-Methylcytosine Modifications

Zeng

Liao

2020

Front. Bioeng. Biotechnol.

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DNA N4-methylcytosine modification (4mC) plays an essential role in a variety of biological processes. Therefore, accurate identification the 4mC distribution in genome-scale is important for systematically understanding its biological functions. In this study, we present Deep4mcPred, a multi-layer deep learning based predictive model to identify DNA N4-methylcytosine modifications. In this predictor, we for the first time integrate residual network and recurrent neural network to build a multi-layer deep learning predictive system. As compared to existing predictors using traditional machine learning, our proposed method has two advantages. First, our deep learning framework does not need to specify the features when training the predictive model. It can automatically learn the high-level features and capture the characteristic specificity of 4mC sites, benefiting to distinguish true 4mC sites from non-4mC sites. On the other hand, our deep learning method outperforms the traditional machine learning predictors in performance by benchmarking comparison, demonstrating that the proposed Deep4mcPred is more effective in the DNA 4mC site prediction. Moreover, via experimental comparison, we found that attention mechanism introduced into the deep learning framework is useful to capture the critical features. Additionally, we develop a webserver implementing the proposed method for the academic use of research community, which is now available at http://server.malab.cn/Deep4mcPred.

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Improved prediction of protein–protein interactions using novel negative samples, features, and an ensemble classifier

Cited by 224 publications

References 42 publications

Computational Methods for Predicting Protein‐Protein Interactions Using Various Protein Features

Computational Methods for Predicting Protein‐Protein Interactions Using Various Protein Features

Predict New Therapeutic Drugs for Hepatocellular Carcinoma Based on Gene Mutation and Expression

Developing a Multi-Layer Deep Learning Based Predictive Model to Identify DNA N4-Methylcytosine Modifications

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