Prediction of RNA-protein interactions using conjoint triad feature and chaos game representation

Wang, Hongchu; Wu, Pengfei

doi:10.1080/21655979.2018.1470721

Cited by 10 publications

(15 citation statements)

References 39 publications

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“…The CT method is used to transform the amino acids in each sequence into numbers according to their volumes and dipoles. Most approaches of PPI [32][33][34] are implemented by the CT method to reduce the extracted features to 7 3 = 343 according to seven classes. Mathematically, we consider a protein sequence P with length L is P = P 1 P 2 P 3 …….…”

Section: Data Preprocessing Stage By Conjoint Triad (Ct) Methodsmentioning

confidence: 99%

Computational predictions for protein sequences of COVID-19 virus via machine learning algorithms

Afify

Zanaty

2021

Med Biol Eng Comput

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The rapid spread of coronavirus disease (COVID-19) has become a worldwide pandemic and affected more than 15 million patients reported in 27 countries. Therefore, the computational biology carrying this virus that correlates with the human population urgently needs to be understood. In this paper, the classification of the human protein sequences of COVID-19, according to the country, is presented based on machine learning algorithms. The proposed model is based on distinguishing 9238 sequences using three stages, including data preprocessing, data labeling, and classification. In the first stage, data preprocessing's function converts the amino acids of COVID-19 protein sequences into eight groups of numbers based on the amino acids' volume and dipole. It is based on the conjoint triad (CT) method. In the second stage, there are two methods for labeling data from 27 countries from 0 to 26. The first method is based on selecting one number for each country according to the code numbers of countries, while the second method is based on binary elements for each country. According to their countries, machine learning algorithms are used to discover different COVID-19 protein sequences in the last stage. The obtained results demonstrate 100% accuracy, 100% sensitivity, and 90% specificity via the country-based binary labeling method with a linear support vector machine (SVM) classifier. Furthermore, with significant infection data, the USA is more prone to correct classification compared to other countries with fewer data. The unbalanced data for COVID-19 protein sequences is considered a major issue, especially as the US's available data represents 76% of a total of 9238 sequences. The proposed model will act as a prediction tool for the COVID-19 protein sequences in different countries.

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Section: Data Preprocessing Stage By Conjoint Triad (Ct) Methodsmentioning

confidence: 99%

Computational predictions for protein sequences of COVID-19 virus via machine learning algorithms

Afify

Zanaty

2021

Med Biol Eng Comput

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“…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

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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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“…rpiCOOL extracts sequencederived features from motif information, repetitive patterns and sequence component, which are further fed into a RF for detecting RNA-protein interactions (Akbaripour-Elahabad et al, 2016). CTF + CGR designs representations derived from chaos game (H. Wang & Wu, 2018). RPiRLS extracts complex features by taking the contextual information into account using kernels (Shen et al, 2018).…”

Section: Conventional Machine Learning-based Methodsmentioning

confidence: 99%

Recent methodology progress of deep learning for RNA–protein interaction prediction

et al. 2019

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Interactions between RNAs and proteins play essential roles in many important biological processes. Benefitting from the advances of next generation sequencing technologies, hundreds of RNA-binding proteins (RBP) and their associated RNAs have been revealed, which enables the large-scale prediction of RNA-protein interactions using machine learning methods. Till now, a wide range of computational tools and pipelines have been developed, including deep learning models, which have achieved remarkable performance on the identification of RNA-protein binding affinities and sites. In this review, we provide an overview of the successful implementation of various deep learning approaches for predicting RNA-protein interactions, mainly focusing on the prediction of RNA-protein interaction pairs and RBP-binding sites on RNAs. Furthermore, we discuss the advantages and disadvantages of these approaches, and highlight future perspectives on how to design better deep learning models. Finally, we suggest some promising future directions of computational tasks in the study of RNA-protein interactions, especially the interactions between noncoding RNAs and proteins. deep learning, feature representation, machine learning, motif discovery, RNA-protein interactions *The first two authors are co-first authors.

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Prediction of RNA-protein interactions using conjoint triad feature and chaos game representation

Cited by 10 publications

References 39 publications

Computational predictions for protein sequences of COVID-19 virus via machine learning algorithms

Computational predictions for protein sequences of COVID-19 virus via machine learning algorithms

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

Recent methodology progress of deep learning for RNA–protein interaction prediction

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