Accurate prediction of nuclear receptors with conjoint triad feature

Wang, Hongchu; Hu, Xuehai

doi:10.1186/s12859-015-0828-1

Cited by 27 publications

(18 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many studies have used conjoint triad to completely explain the essential PPI details to represent the properties of amino acid [ 34 , 35 ]. In the conjoint triad system, the 20 amino acids are divided into seven classes based on their amounts of dipoles and side chains [ Table 2 ].…”

Section: Methodsmentioning

confidence: 99%

Machine learning techniques for sequence-based prediction of viral–host interactions between SARS-CoV-2 and human proteins

2020

View full text Add to dashboard Cite

Background COVID-19 (Coronavirus Disease-19), a disease caused by the SARS-CoV-2 virus, has been declared as a pandemic by the World Health Organization on March 11, 2020. Over 15 million people have already been affected worldwide by COVID-19, resulting in more than 0.6 million deaths. Protein–protein interactions (PPIs) play a key role in the cellular process of SARS-CoV-2 virus infection in the human body. Recently a study has reported some SARS-CoV-2 proteins that interact with several human proteins while many potential interactions remain to be identified. Method In this article, various machine learning models are built to predict the PPIs between the virus and human proteins that are further validated using biological experiments. The classification models are prepared based on different sequence-based features of human proteins like amino acid composition, pseudo amino acid composition, and conjoint triad. Result We have built an ensemble voting classifier using SVM Radial , SVM Polynomial , and Random Forest technique that gives a greater accuracy, precision, specificity, recall, and F1 score compared to all other models used in the work. A total of 1326 potential human target proteins of SARS-CoV-2 have been predicted by the proposed ensemble model and validated using gene ontology and KEGG pathway enrichment analysis. Several repurposable drugs targeting the predicted interactions are also reported. Conclusion This study may encourage the identification of potential targets for more effective anti-COVID drug discovery.

show abstract

Section: Methodsmentioning

confidence: 99%

Machine learning techniques for sequence-based prediction of viral–host interactions between SARS-CoV-2 and human proteins

2020

View full text Add to dashboard Cite

show abstract

“…Thus, each protein sequence is represented by a 343- (7 × 7 × 7) dimensional vector, where each element of the vector corresponds to the frequency of the corresponding conjoint triad in the protein sequence. The conjoint triad feature (CTF) has successfully predicted enzyme function [ 44 ], protein-protein interactions [ 45 ], RNA-protein interactions [ 46 ], and nuclear receptors [ 47 ]. The features of CTF can be formulated as follows:

where n i is the occurrence number of each triad type of the protein sequence, L is the length of the protein sequence.…”

Section: Methodsmentioning

confidence: 99%

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

Jing

2020

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

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.

show abstract

“…A comprehensive review of protein attribute prediction [ 27 ] stated that besides a reliable and objective benchmark protein sequence dataset, the perfect formulation of protein sample is necessary for the development of a high-throughput automated predictive tool. The simplest and also most popular approach to formulate protein sequences is amino acid composition (AAC) [ 28 , 29 ] which uses the normalized frequency of each amino acid in one protein sample. The conjoint triad feature [ 19 , 28 ] encodes each protein sequence by using a triad frequency distribution.…”

Section: Methodsmentioning

confidence: 99%

Sequence-based predictive modeling to identify cancerlectins

Lai

Chen

et al. 2017

Oncotarget

View full text Add to dashboard Cite

Lectins are a diverse type of glycoproteins or carbohydrate-binding proteins that have a wide distribution to various species. They can specially identify and exclusively bind to a certain kind of saccharide groups. Cancerlectins are a group of lectins that are closely related to cancer and play a major role in the initiation, survival, growth, metastasis and spread of tumor. Several computational methods have emerged to discriminate cancerlectins from non-cancerlectins, which promote the study on pathogenic mechanisms and clinical treatment of cancer. However, the predictive accuracies of most of these techniques are very limited. In this work, by constructing a benchmark dataset based on the CancerLectinDB database, a new amino acid sequence-based strategy for feature description was developed, and then the binomial distribution was applied to screen the optimal feature set. Ultimately, an SVM-based predictor was performed to distinguish cancerlectins from non-cancerlectins, and achieved an accuracy of 77.48% with AUC of 85.52% in jackknife cross-validation. The results revealed that our prediction model could perform better comparing with published predictive tools.

show abstract

Accurate prediction of nuclear receptors with conjoint triad feature

Cited by 27 publications

References 42 publications

Machine learning techniques for sequence-based prediction of viral–host interactions between SARS-CoV-2 and human proteins

Machine learning techniques for sequence-based prediction of viral–host interactions between SARS-CoV-2 and human proteins

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

Sequence-based predictive modeling to identify cancerlectins

Contact Info

Product

Resources

About