Prediction of protein interactions by combining genetic algorithm with SVM method

Wang, Bing; Ge, Liu; Jia, Wenyou; Liu, Li; Chen, Fuchun

doi:10.1109/cec.2007.4424488

Cited by 6 publications

(4 citation statements)

References 35 publications

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“…Among them, many machine learning methods have been developed or adopted, such as those using support vector machine (SVM) [ 16 , 17 , 19 - 22 ], neural network [ 13 - 15 , 23 , 24 ], genetic algorithm [ 25 , 26 ], hidden Markov models [ 27 ], Bayesian networks [ 28 , 29 ], random forests [ 30 , 31 ], and so on.…”

Section: Introductionmentioning

confidence: 99%

Sequence-based identification of interface residues by an integrative profile combining hydrophobic and evolutionary information

Chen

2010

BMC Bioinformatics

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BackgroundProtein-protein interactions play essential roles in protein function determination and drug design. Numerous methods have been proposed to recognize their interaction sites, however, only a small proportion of protein complexes have been successfully resolved due to the high cost. Therefore, it is important to improve the performance for predicting protein interaction sites based on primary sequence alone.ResultsWe propose a new idea to construct an integrative profile for each residue in a protein by combining its hydrophobic and evolutionary information. A support vector machine (SVM) ensemble is then developed, where SVMs train on different pairs of positive (interface sites) and negative (non-interface sites) subsets. The subsets having roughly the same sizes are grouped in the order of accessible surface area change before and after complexation. A self-organizing map (SOM) technique is applied to group similar input vectors to make more accurate the identification of interface residues. An ensemble of ten-SVMs achieves an MCC improvement by around 8% and F1 improvement by around 9% over that of three-SVMs. As expected, SVM ensembles constantly perform better than individual SVMs. In addition, the model by the integrative profiles outperforms that based on the sequence profile or the hydropathy scale alone. As our method uses a small number of features to encode the input vectors, our model is simpler, faster and more accurate than the existing methods.ConclusionsThe integrative profile by combining hydrophobic and evolutionary information contributes most to the protein-protein interaction prediction. Results show that evolutionary context of residue with respect to hydrophobicity makes better the identification of protein interface residues. In addition, the ensemble of SVM classifiers improves the prediction performance.AvailabilityDatasets and software are available at http://mail.ustc.edu.cn/~bigeagle/BMCBioinfo2010/index.htm.

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Section: Introductionmentioning

confidence: 99%

Sequence-based identification of interface residues by an integrative profile combining hydrophobic and evolutionary information

Chen

2010

BMC Bioinformatics

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“…The second category is the machine learning–based method, which also can be divided into supervised learning and semi-supervised learning ( Cai et al, 2018 ; Chen et al, 2018a ). All the supervised learning methods such as Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM) ( Wang et al, 2007b ), Gradient Boosting Decision Tree (GBDT), and Neural Network (NN) ( Gao et al, 2021 ) require both positive and negative samples for training ( Chen et al, 2018a ). In contrast, the semi-supervised learning-based methods can train the model without negative samples.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Matrix Completion Model for Predicting Potential Drugs Against SARS-CoV-2

2021

Front. Microbiol.

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Because of the catastrophic outbreak of global coronavirus disease 2019 (COVID-19) and its strong infectivity and possible persistence, computational repurposing of existing approved drugs will be a promising strategy that facilitates rapid clinical treatment decisions and provides reasonable justification for subsequent clinical trials and regulatory reviews. Since the effects of a small number of conditionally marketed vaccines need further clinical observation, there is still an urgent need to quickly and effectively repurpose potentially available drugs before the next disease peak. In this work, we have manually collected a set of experimentally confirmed virus-drug associations through the publicly published database and literature, consisting of 175 drugs and 95 viruses, as well as 933 virus-drug associations. Then, because the samples are extremely sparse and unbalanced, negative samples cannot be easily obtained. We have developed an ensemble model, EMC-Voting, based on matrix completion and weighted soft voting, a semi-supervised machine learning model for computational drug repurposing. Finally, we have evaluated the prediction performance of EMC-Voting by fivefold crossing-validation and compared it with other baseline classifiers and prediction models. The case study for the virus SARS-COV-2 included in the dataset demonstrates that our model achieves the outperforming AUPR value of 0.934 in virus-drug association’s prediction.

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“…The proposed tool was applied to a dataset extracted using HINT-KB (http://150.140.142:84) which is a publicly available database for Human PPI data. In order to test its efficiency we compared it with two modern PPI prediction methods: A Random Forest method [3][4][5][6] and the wrapper methodology combining Genetic Algorithms and SVM Classifiers [7,8].…”

Section: Introductionmentioning

confidence: 99%

Efficient Computational Prediction and Scoring of Human Protein-Protein Interactions Using a Novel Gene Expression Programming Methodology

Theofilatos

Dimitrakopoulos

Antoniou

et al. 2012

Engineering Applications of Neural Networks

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Abstract. Proteins and their interactions have been proven to play a central role in many cellular processes. Thus, many experimental methods have been developed for their prediction. These experimental methods are uneconomic and time consuming in the case of low throughput methods or inaccurate in the case of high throughput methods. To overcome these limitations, many computational methods have been developed to predict and score ProteinProtein Interactions (PPIs) using a variety of functional, sequential and structural data for each protein pair. Existing computational methods can still be enhanced in terms of classification performance and interpretability. In the present paper we present a novel Gene Expression Programming (GEP) algorithm, named as jGEPModelling 2.0, and apply it to the problem of PPI prediction and scoring. jGEPModelling2.0 is a variation of the classic GEP algorithm to make it suitable for the problem of PPI prediction and enhance its classification performance. To test its efficiency, we applied it to a public available dataset and compared it to two other state-of-the-art PPI prediction models. Experimental results proved that jGEPModelling2.0 outperformed existing methodologies in terms of classification performance and interpretability. (This paper is submitted for the CIAB2012 workshop).

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Prediction of protein interactions by combining genetic algorithm with SVM method

Cited by 6 publications

References 35 publications

Sequence-based identification of interface residues by an integrative profile combining hydrophobic and evolutionary information

Sequence-based identification of interface residues by an integrative profile combining hydrophobic and evolutionary information

An Ensemble Matrix Completion Model for Predicting Potential Drugs Against SARS-CoV-2

Efficient Computational Prediction and Scoring of Human Protein-Protein Interactions Using a Novel Gene Expression Programming Methodology

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