Support vector machines for predicting protein-protein interactions using domains and hydrophobicity features

Alashwal, Hany; Deris, Safaai; Othman, Razib M.

doi:10.1109/icoci.2006.5276519

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…; X m ,Y), where X{X1,X 2, X 3, ….,X m } is the m-dimensional input variable and Y is the output variable taking {0,1}. As input, this method can take either protein interaction datasets or genomic interaction datasets ( Jansen et al, 2003 ; Alashwal, Deris and Othman, 2009 ; Lin et al, 2021 ). In the end, the classifier gives a binary response, a zero indicating the interaction is not verified, and a one when there is a potential interaction.…”

Section: Methods Based On the Machine Learning Algorithmmentioning

confidence: 99%

Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context

Robin

Bodein

Scott‐Boyer

et al. 2022

Front. Mol. Biosci.

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At the heart of the cellular machinery through the regulation of cellular functions, protein–protein interactions (PPIs) have a significant role. PPIs can be analyzed with network approaches. Construction of a PPI network requires prediction of the interactions. All PPIs form a network. Different biases such as lack of data, recurrence of information, and false interactions make the network unstable. Integrated strategies allow solving these different challenges. These approaches have shown encouraging results for the understanding of molecular mechanisms, drug action mechanisms, and identification of target genes. In order to give more importance to an interaction, it is evaluated by different confidence scores. These scores allow the filtration of the network and thus facilitate the representation of the network, essential steps to the identification and understanding of molecular mechanisms. In this review, we will discuss the main computational methods for predicting PPI, including ones confirming an interaction as well as the integration of PPIs into a network, and we will discuss visualization of these complex data.

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Section: Methods Based On the Machine Learning Algorithmmentioning

confidence: 99%

Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context

Robin

Bodein

Scott‐Boyer

et al. 2022

Front. Mol. Biosci.

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show abstract

“…The nu-SVR is a model of LibSVM [12], which can be available at , it has been widely used in data classification. By setting the appropriate parameters, the SVR algorithm can achieve a reasonable result [13]. GA is a computational model for simulating the evolutionary process of natural selection and genetic mechanism based on the theory of biological evolution [14].…”

Section: Algorithmmentioning

confidence: 99%

Prediction of RBPs from sequence by means of genetic algorithm and nu-SVR

Yang

2022

J. Phys.: Conf. Ser.

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Post-transcriptional regulation occurs at every moment in human’s body, so it makes the identification of RNA-binding proteins (RBPs) very important, because the RBPs are indispensable accessories to post-transcriptional regulation. Although many computational methods have been developed to replace the high-costly experimental methods, most of them run slowly and the result not well enough. Based on above factors, in this study, we propose a new method namely GASVR-RBP. Firstly, we extract features from protein sequences based on physicochemical properties and Pre-in-One web server, after the feature vector space constructed, we trained eight classifiers on 9857 protein sequences with the combination of genetic algorithm (GA) and nu-SVR, and by employing the ensemble strategy, we obtained an improved performance in three test set, the accuracy are 89.3%, 84.3% and 88.8%, which higher than Naive Bayes (NB) and Random Forest (RF). These results show that our method is effective for RBPs prediction.

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Vision-based obstacle detection using a support vector machine

Ubbens

Schuurman

2009

2009 Canadian Conference on Electrical and Computer Engineering

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Support vector machines for predicting protein-protein interactions using domains and hydrophobicity features

Cited by 5 publications

References 17 publications

Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context

Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context

Prediction of RBPs from sequence by means of genetic algorithm and nu-SVR

Vision-based obstacle detection using a support vector machine

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