Prediction of Protein-Protein Interactions Based on Protein-Protein Correlation Using Least Squares Regression

Huang, De-Shuang; Zhang, Lei; Han, Kyungsook; Deng, Su-Ping; Yang, Kai

doi:10.2174/1389203715666140724084019

Cited by 102 publications

(26 citation statements)

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“…Secondly, the known miRNA-disease associations with experimental evidences are still insufficient. The prediction performance of CMFMDA will be improved by integrating more reliable biological information [77][78][79][80][81][82][83][84][85][86]. Finally, how to more reasonably extract and integrate information from biological datasets should be investigated in the future.…”

Section: Discussionmentioning

confidence: 99%

miRNA-Disease Association Prediction with Collaborative Matrix Factorization

et al. 2017

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As one of the factors in the noncoding RNA family, microRNAs (miRNAs) are involved in the development and progression of various complex diseases. Experimental identification of miRNA-disease association is expensive and time-consuming. Therefore, it is necessary to design efficient algorithms to identify novel miRNA-disease association. In this paper, we developed the computational method of Collaborative Matrix Factorization for miRNA-Disease Association prediction (CMFMDA) to identify potential miRNA-disease associations by integrating miRNA functional similarity, disease semantic similarity, and experimentally verified miRNA-disease associations. Experiments verified that CMFMDA achieves intended purpose and application values with its short consuming-time and high prediction accuracy. In addition, we used CMFMDA on Esophageal Neoplasms and Kidney Neoplasms to reveal their potential related miRNAs. As a result, 84% and 82% of top 50 predicted miRNA-disease pairs for these two diseases were confirmed by experiment. Not only this, but also CMFMDA could be applied to new diseases and new miRNAs without any known associations, which overcome the defects of many previous computational methods.

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Section: Discussionmentioning

confidence: 99%

miRNA-Disease Association Prediction with Collaborative Matrix Factorization

et al. 2017

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“…After data preprocessing, the real biological dataset contains 1368 samples [48,49]. Of these, 836 samples were identified case studies; the remaining 532 samples were normal sample [50,51]. Each sample of real biological dataset contains 309,316 SNPs with genotype information, APOE status, and LOAD status [52].…”

Section: Application To Real Snp Datasetmentioning

confidence: 99%

FAACOSE: A Fast Adaptive Ant Colony Optimization Algorithm for Detecting SNP Epistasis

Lin

Yuan²,

Huang

2017

Complexity

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The epistasis is prevalent in the SNP interactions. Some of the existing methods are focused on constructing models for two SNPs. Other methods only find the SNPs in consideration of one-objective function. In this paper, we present a unified fast framework integrating adaptive ant colony optimization algorithm with multiobjective functions for detecting SNP epistasis in GWAS datasets. We compared our method with other existing methods using synthetic datasets and applied the proposed method to Late-Onset Alzheimer's Disease dataset. Our experimental results show that the proposed method outperforms other methods in epistasis detection, and the result of real dataset contributes to the research of mechanism underlying the disease.

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“…Consequently, large quantities of protein sequence-based methods for predicting PPIs have been exploited. [9][10][11][12][13][14] Such as, Sylvain et al 15 proposed a novel protein-protein interaction prediction engine called PIPE, which can detect PPIs for any target pair of the yeast Saccharomyces cerevisiae proteins. Xia et al 16 proposed a sequence-based method that selected rotation forest as classifier and employed autocorrelation descriptor as feature extraction method for predicting PPIs.…”

Section: Introductionmentioning

confidence: 99%

Improving protein–protein interactions prediction accuracy using protein evolutionary information and relevance vector machine model

Meng

You

et al. 2016

Protein Science

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Predicting protein–protein interactions (PPIs) is a challenging task and essential to construct the protein interaction networks, which is important for facilitating our understanding of the mechanisms of biological systems. Although a number of high‐throughput technologies have been proposed to predict PPIs, there are unavoidable shortcomings, including high cost, time intensity, and inherently high false positive rates. For these reasons, many computational methods have been proposed for predicting PPIs. However, the problem is still far from being solved. In this article, we propose a novel computational method called RVM‐BiGP that combines the relevance vector machine (RVM) model and Bi‐gram Probabilities (BiGP) for PPIs detection from protein sequences. The major improvement includes (1) Protein sequences are represented using the Bi‐gram probabilities (BiGP) feature representation on a Position Specific Scoring Matrix (PSSM), in which the protein evolutionary information is contained; (2) For reducing the influence of noise, the Principal Component Analysis (PCA) method is used to reduce the dimension of BiGP vector; (3) The powerful and robust Relevance Vector Machine (RVM) algorithm is used for classification. Five‐fold cross‐validation experiments executed on yeast and Helicobacter pylori datasets, which achieved very high accuracies of 94.57 and 90.57%, respectively. Experimental results are significantly better than previous methods. To further evaluate the proposed method, we compare it with the state‐of‐the‐art support vector machine (SVM) classifier on the yeast dataset. The experimental results demonstrate that our RVM‐BiGP method is significantly better than the SVM‐based method. In addition, we achieved 97.15% accuracy on imbalance yeast dataset, which is higher than that of balance yeast dataset. The promising experimental results show the efficiency and robust of the proposed method, which can be an automatic decision support tool for future proteomics research. For facilitating extensive studies for future proteomics research, we developed a freely available web server called RVM‐BiGP‐PPIs in Hypertext Preprocessor (PHP) for predicting PPIs. The web server including source code and the datasets are available at http://219.219.62.123:8888/BiGP/.

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Prediction of Protein-Protein Interactions Based on Protein-Protein Correlation Using Least Squares Regression

Cited by 102 publications

References 0 publications

miRNA-Disease Association Prediction with Collaborative Matrix Factorization

miRNA-Disease Association Prediction with Collaborative Matrix Factorization

FAACOSE: A Fast Adaptive Ant Colony Optimization Algorithm for Detecting SNP Epistasis

Improving protein–protein interactions prediction accuracy using protein evolutionary information and relevance vector machine model

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