Prediction of microRNA-binding residues in protein using a Laplacian support vector machine based on sequence information

2018 ◽  
Vol 16 (03) ◽  
pp. 1840009 ◽  
Author(s):  
Xin Ma ◽  
Jing Guo ◽  
Xiao Sun
Keyword(s):  
Support Vector Machine ◽  
Evolutionary Information ◽  
Support Vector ◽  
Sequence Information ◽  
Protein Residues ◽  
Research Areas ◽  
Binding Residue ◽  
Structure Conservation ◽  
Binding Residues ◽  
Binding Residue Prediction

The identification of microRNA (miRNA)-binding protein residues significantly impacts several research areas, including gene regulation and expression. We propose a method, PmiRBR, which combines a novel hybrid feature with the Laplacian support vector machine (LapSVM) algorithm to predict miRNA-binding residues in protein sequences. The hybrid feature is constituted by secondary structure, conservation scores, and a novel feature, which includes evolutionary information combined with the physicochemical properties of amino acids. Performance comparisons of the various features indicate that our novel feature contributes the most to prediction improvement. Our results demonstrate that PmiRBR can achieve 85.96% overall accuracy, with 43.89% sensitivity and 90.56% specificity. PmiRBR significantly outperforms other approaches at miRNA-binding residue prediction.

scholarly journals Identification of DNA-Binding Proteins Using Support Vector Machine with Sequence Information

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Xin Ma ◽  
Jiansheng Wu ◽  
Xiaoyun Xue
Keyword(s):  
Support Vector Machine ◽  
Dna Binding ◽  
Binding Proteins ◽  
Query Protein ◽  
Dna Binding Proteins ◽  
Evolutionary Information ◽  
Support Vector ◽  
Sequence Information ◽  
Novel Approach ◽  
Matthew’S Correlation Coefficient

DNA-binding proteins are fundamentally important in understanding cellular processes. Thus, the identification of DNA-binding proteins has the particularly important practical application in various fields, such as drug design. We have proposed a novel approach method for predicting DNA-binding proteins using only sequence information. The prediction model developed in this study is constructed by support vector machine-sequential minimal optimization (SVM-SMO) algorithm in conjunction with a hybrid feature. The hybrid feature is incorporating evolutionary information feature, physicochemical property feature, and two novel attributes. These two attributes use DNA-binding residues and nonbinding residues in a query protein to obtain DNA-binding propensity and nonbinding propensity. The results demonstrate that our SVM-SMO model achieves 0.67 Matthew's correlation coefficient (MCC) and 89.6% overall accuracy with 88.4% sensitivity and 90.8% specificity, respectively. Performance comparisons on various features indicate that two novel attributes contribute to the performance improvement. In addition, our SVM-SMO model achieves the best performance than state-of-the-art methods on independent test dataset.

Improving PSI-BLAST’s Fold Recognition Performance through Combining Consensus Sequences and Support Vector Machine

2011 ◽  
pp. 51-59
Author(s):  
Ren-Xiang Yan ◽  
Jing Liu ◽  
Yi-Min Tao
Keyword(s):  
Support Vector Machine ◽  
Sequence Alignment ◽  
Recognition Performance ◽  
Consensus Sequence ◽  
Early Time ◽  
Fold Recognition ◽  
Support Vector ◽  
Sequence Information ◽  
Consensus Sequences ◽  
Profile Alignment

Profile-profile alignment may be the most sensitive and useful computational resource for identifying remote homologies and recognizing protein folds. However, profile-profile alignment is usually much more complex and slower than sequence-sequence or profile-sequence alignment. The profile or PSSM (position-specific scoring matrix) can be used to represent the mutational variability at each sequence position of a protein by using a vector of amino acid substitution frequencies and it is a much richer encoding of a protein sequence. Consensus sequence, which can be considered as a simplified profile, was used to improve sequence alignment accuracy in the early time. Recently, several studies were carried out to improve PSI-BLAST’s fold recognition performance by using consensus sequence information. There are several ways to compute a consensus sequence. Based on these considerations, we propose a method that combines the information of different types of consensus sequences with the assistance of support vector machine learning in this chapter. Benchmark results suggest that our method can further improve PSI-BLAST’s fold recognition performance.

Using the Chou’s Pseudo Component to Predict the ncRNA Locations Based on the Improved K-Nearest Neighbor (iKNN) Classifier

2020 ◽  
Vol 15 (6) ◽  
pp. 563-573
Author(s):  
Chengyan Wu ◽  
Qianzhong Li ◽  
Ru Xing ◽  
Guo-Liang Fan
Keyword(s):  
Decision Making ◽  
Support Vector Machine ◽  
Secondary Structure ◽  
Feature Selection Method ◽  
Support Vector ◽  
Sequence Information ◽  
Diversity Combining ◽  
Machine Method ◽  
Increment Of Diversity ◽  
Jackknife Test

Background: The non-coding RNA identification at the organelle genome level is a challenging task. In our previous work, an ncRNA dataset with less than 80% sequence identity was built, and a method incorporating an increment of diversity combining with support vector machine method was proposed. Objective: Based on the ncRNA_361 dataset, a novel decision-making method-an improved KNN (iKNN) classifier was proposed. Methods: In this paper, based on the iKNN algorithm, the physicochemical features of nucleotides, the degeneracy of genetic codons, and topological secondary structure were selected to represent the effective ncRNA characters. Then, the incremental feature selection method was utilized to optimize the feature set. Results: The results of iKNN indicated that the decision-making method of mean value is distinctly superior to the traditional decision-making method of majority vote the Increment of Diversity Combining Support Vector Machine (ID-SVM). The iKNN algorithm achieved an overall accuracy of 97.368% in the jackknife test, when k=3. Conclusion: It should be noted that the triplets of the structure-sequence mode under reading frames not only contains the entire sequence information but also reflects whether the base was paired or not, and the secondary structural topological parameters further describe the ncRNA secondary structure on the spatial level. The ncRNA dataset and the iKNN classifier are freely available at http://202.207.14.87:8032/fuwu/iKNN/index.asp.

scholarly journals Drug-Target Interaction Prediction Based on Drug Fingerprint Information and Protein Sequence

Molecules ◽  
2019 ◽  
Vol 24 (16) ◽  
pp. 2999 ◽  
Author(s):  
Yang Li ◽  
Yu-An Huang ◽  
Zhu-Hong You ◽  
Li-Ping Li ◽  
Zheng Wang
Keyword(s):  
Drug Target ◽  
Large Scale ◽  
Protein Sequences ◽  
Performance Comparison ◽  
Target Pair ◽  
Evolutionary Information ◽  
Support Vector ◽  
Sequence Information ◽  
Rotation Forest ◽  
Comparison Results

The identification of drug-target interactions (DTIs) is a critical step in drug development. Experimental methods that are based on clinical trials to discover DTIs are time-consuming, expensive, and challenging. Therefore, as complementary to it, developing new computational methods for predicting novel DTI is of great significance with regards to saving cost and shortening the development period. In this paper, we present a novel computational model for predicting DTIs, which uses the sequence information of proteins and a rotation forest classifier. Specifically, all of the target protein sequences are first converted to a position-specific scoring matrix (PSSM) to retain evolutionary information. We then use local phase quantization (LPQ) descriptors to extract evolutionary information in the PSSM. On the other hand, substructure fingerprint information is utilized to extract the features of the drug. We finally combine the features of drugs and protein together to represent features of each drug-target pair and use a rotation forest classifier to calculate the scores of interaction possibility, for a global DTI prediction. The experimental results indicate that the proposed model is effective, achieving average accuracies of 89.15%, 86.01%, 82.20%, and 71.67% on four datasets (i.e., enzyme, ion channel, G protein-coupled receptors (GPCR), and nuclear receptor), respectively. In addition, we compared the prediction performance of the rotation forest classifier with another popular classifier, support vector machine, on the same dataset. Several types of methods previously proposed are also implemented on the same datasets for performance comparison. The comparison results demonstrate the superiority of the proposed method to the others. We anticipate that the proposed method can be used as an effective tool for predicting drug-target interactions on a large scale, given the information of protein sequences and drug fingerprints.

SMpred: A Support Vector Machine Approach to Identify Structural Motifs in Protein Structure Without Using Evolutionary Information

2010 ◽  
Vol 28 (3) ◽  
pp. 405-414 ◽  
Author(s):  
Ganesan Pugalenthi ◽  
Krishna Kumar Kandaswamy ◽  
P. N. Suganthan ◽  
R. Sowdhamini ◽  
Thomas Martinetz ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Protein Structure ◽  
Evolutionary Information ◽  
Support Vector ◽  
Structural Motifs

Improving PSI-BLAST’s Fold Recognition Performance through Combining Consensus Sequences and Support Vector Machine

2013 ◽  
pp. 1667-1675
Author(s):  
Ren-Xiang Yan ◽  
Jing Liu ◽  
Yi-Min Tao
Keyword(s):  
Support Vector Machine ◽  
Sequence Alignment ◽  
Recognition Performance ◽  
Consensus Sequence ◽  
Early Time ◽  
Fold Recognition ◽  
Support Vector ◽  
Sequence Information ◽  
Consensus Sequences ◽  
Profile Alignment

Profile-profile alignment may be the most sensitive and useful computational resource for identifying remote homologies and recognizing protein folds. However, profile-profile alignment is usually much more complex and slower than sequence-sequence or profile-sequence alignment. The profile or PSSM (position-specific scoring matrix) can be used to represent the mutational variability at each sequence position of a protein by using a vector of amino acid substitution frequencies and it is a much richer encoding of a protein sequence. Consensus sequence, which can be considered as a simplified profile, was used to improve sequence alignment accuracy in the early time. Recently, several studies were carried out to improve PSI-BLAST’s fold recognition performance by using consensus sequence information. There are several ways to compute a consensus sequence. Based on these considerations, we propose a method that combines the information of different types of consensus sequences with the assistance of support vector machine learning in this chapter. Benchmark results suggest that our method can further improve PSI-BLAST’s fold recognition performance.

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