A MapReduce based parallel SVM for large-scale predicting protein–protein interactions

You, Zhu-Hong; Yu, Jian; Zhu, Lin; Li, Shuai; Wen, Zhen

doi:10.1016/j.neucom.2014.05.072

Cited by 112 publications

(41 citation statements)

References 32 publications

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“…Finally, we used the remaining 3899 protein-protein pairs of experimentally verified PPIs from 2502 different human proteins to comprise the golden standard positive dataset. For golden standard negative dataset, we then followed the previous work [12] assuming the proteins in different subcellular compartments do not interact with each other. By this way, we finally obtained 4262 protein pairs from 661 different human proteins as the negative dataset.…”

Section: Methodsmentioning

confidence: 99%

Improved protein-protein interactions prediction via weighted sparse representation model combining continuous wavelet descriptor and PseAA composition

et al. 2016

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BackgroundProtein-protein interactions (PPIs) are essential to most biological processes. Since bioscience has entered into the era of genome and proteome, there is a growing demand for the knowledge about PPI network. High-throughput biological technologies can be used to identify new PPIs, but they are expensive, time-consuming, and tedious. Therefore, computational methods for predicting PPIs have an important role. For the past years, an increasing number of computational methods such as protein structure-based approaches have been proposed for predicting PPIs. The major limitation in principle of these methods lies in the prior information of the protein to infer PPIs. Therefore, it is of much significance to develop computational methods which only use the information of protein amino acids sequence.ResultsHere, we report a highly efficient approach for predicting PPIs. The main improvements come from the use of a novel protein sequence representation by combining continuous wavelet descriptor and Chou’s pseudo amino acid composition (PseAAC), and from adopting weighted sparse representation based classifier (WSRC). This method, cross-validated on the PPIs datasets of Saccharomyces cerevisiae, Human and H. pylori, achieves an excellent results with accuracies as high as 92.50%, 95.54% and 84.28% respectively, significantly better than previously proposed methods. Extensive experiments are performed to compare the proposed method with state-of-the-art Support Vector Machine (SVM) classifier.ConclusionsThe outstanding results yield by our model that the proposed feature extraction method combing two kinds of descriptors have strong expression ability and are expected to provide comprehensive and effective information for machine learning-based classification models. In addition, the prediction performance in the comparison experiments shows the well cooperation between the combined feature and WSRC. Thus, the proposed method is a very efficient method to predict PPIs and may be a useful supplementary tool for future proteomics studies.

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Section: Methodsmentioning

confidence: 99%

Improved protein-protein interactions prediction via weighted sparse representation model combining continuous wavelet descriptor and PseAA composition

et al. 2016

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“…We have prepared a corpus of 100,000 and 1,000,000 Uyghur sentences to carry out experiments. The previous works showed that the performance of learners can benefit significantly from much larger training sets [21][22][23][24][25][26][27][28][29]. The corpus covers the fields of news, entertainment, and sports.…”

Section: Design Of the Experimentsmentioning

confidence: 99%

Construction of the Uyghur Noun Morphological Re-Inflection Model Based on Hybrid Strategy

Munire

Yang

2019

Applied Sciences

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In this paper, a hybrid strategy of rules and statistics is employed to implement the Uyghur Noun Re-inflection model. More specifically, completed Uyghur sentences are taken as an input, and these Uyghur sentences are marked with part of speech tagging, and the nouns in the sentences remain the form of the stem. In this model, relevant linguistic rules and statistical algorithms are used to find the most probable noun suffixes and output the Uyghur sentences after the nouns are re-inflected. With rules of linguistics artificially summed up, the training corpora are formed by the human–machine exchange. The final experimental result shows that the Uyghur morphological re-inflection model is of high performance and can be applied to various fields of natural language processing, such as Uyghur machine translation and natural language generation.

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“…To construct a golden standard positive dataset, we chose the remaining 3899 interacting protein pairs among 2502 different Human proteins. Because the proteins in diverse subcellular fractions cannot interact with each other, we built a golden standard negative dataset by selecting 4262 protein pairs among 661 distinct Human proteins [49]. Finally, the Human dataset was composed of 8161 protein pairs.…”

Section: Datasets Constructionmentioning

confidence: 99%

An Ensemble Classifier with Random Projection for Predicting Protein–Protein Interactions Using Sequence and Evolutionary Information

et al. 2018

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Identifying protein-protein interactions (PPIs) is crucial to comprehend various biological processes in cells. Although high-throughput techniques generate many PPI data for various species, they are only a petty minority of the entire PPI network. Furthermore, these approaches are costly and time-consuming and have a high error rate. Therefore, it is necessary to design computational methods for efficiently detecting PPIs. In this study, a random projection ensemble classifier (RPEC) was explored to identify novel PPIs using evolutionary information contained in protein amino acid sequences. The evolutionary information was obtained from a position-specific scoring matrix (PSSM) generated from PSI-BLAST. A novel feature fusion scheme was then developed by combining discrete cosine transform (DCT), fast Fourier transform (FFT), and singular value decomposition (SVD). Finally, via the random projection ensemble classifier, the performance of the presented approach was evaluated on Yeast, Human, and H. pylori PPI datasets using 5-fold cross-validation. Our approach achieved high prediction accuracies of 95.64%, 96.59%, and 87.62%, respectively, effectively outperforming other existing methods. Generally speaking, our approach is quite promising and supplies a practical and effective method for predicting novel PPIs.

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A MapReduce based parallel SVM for large-scale predicting protein–protein interactions

Cited by 112 publications

References 32 publications

Improved protein-protein interactions prediction via weighted sparse representation model combining continuous wavelet descriptor and PseAA composition

Improved protein-protein interactions prediction via weighted sparse representation model combining continuous wavelet descriptor and PseAA composition

Construction of the Uyghur Noun Morphological Re-Inflection Model Based on Hybrid Strategy

An Ensemble Classifier with Random Projection for Predicting Protein–Protein Interactions Using Sequence and Evolutionary Information

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