Sequence-Based Prediction of Protein-Protein Interactions by Means of Rotation Forest and Autocorrelation Descriptor

Xia, Junfeng; Han, Kyungsook; Huang, De-Shuang

doi:10.2174/092986610789909403

Cited by 147 publications

(89 citation statements)

References 69 publications

(105 reference statements)

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“…We intend to filter out the incorrect data before finding motifs. It is possible to evaluate the reliability of protein interactions by using computational and mathematical methods including geometric method [33], domain-based method [34], sequence-based method [35,36], and structurebased method [37]. In further research, we will try to apply the penalized matrix decomposition [38] to biological network to discover larger network motifs.…”

Section: Discussionmentioning

confidence: 99%

CombiMotif: A new algorithm for network motifs discovery in protein–protein interaction networks

Luo

Duan

et al. 2014

Physica A: Statistical Mechanics and its Applications

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Section: Discussionmentioning

confidence: 99%

CombiMotif: A new algorithm for network motifs discovery in protein–protein interaction networks

Luo

Duan

et al. 2014

Physica A: Statistical Mechanics and its Applications

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“…In this section, we compared the results of the proposed method with those of the existing methods on the H. pylori data set. The results of 10-fold cross-validation over several different methods 11,21,[41][42][43][44] on the H. pylori data set are shown in Table 6. In Boch and Gough's approach, 41,45 several structural and physiochemical descriptors with SVM as the classifier were used to predict PPIs.…”

Section: Compared With Other Methodsmentioning

confidence: 99%

Prediction of Protein–Protein Interactions with Physicochemical Descriptors and Wavelet Transform via Random Forests

Xiao

Liu

2016

SLAS Technology

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Protein-protein interactions (PPIs) provide valuable insight into the inner workings of cells, and it is significant to study the network of PPIs. It is vitally important to develop an automated method as a high-throughput tool to timely predict PPIs. Based on the physicochemical descriptors, a protein was converted into several digital signals, and then wavelet transform was used to analyze them. With such a formulation frame to represent the samples of protein sequences, the random forests algorithm was adopted to conduct prediction. The results on a large-scale independent-test data set show that the proposed model can achieve a good performance with an accuracy value of about 0.86 and a geometric mean value of about 0.85. Therefore, it can be a usefully supplementary tool for PPI prediction. The predictor used in this article is freely available at http://www.jci-bioinfo.cn/PPI_RF.

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“…Figure 4 shows a bit more details of C, T and D on a protein region sequence with 21 amino acids. There are also some other methods that extract different types of concerned features for protein sequence, for example, Moran Autocorrelation Score [13], and Amino Acid Triplet [14]. Protein sequence information is the main information directly linked to PHPPI.…”

Section: A Sequence Informationmentioning

confidence: 99%

Towards Data Analytics of Pathogen-Host Protein-Protein Interaction: A Survey

Chen

Shen

Wang

et al. 2016

2016 IEEE International Congress on Big Data (BigData Congress)

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Abstract"Big Data" is immersed in many disciplines, including computer vision, economics, online resources, bioinformatics and so on. Increasing researches are conducted on data mining and machine learning for uncovering and predicting related domain knowledge. Protein-protein interaction is one of the main areas in bioinformatics as it is the basis of the biological functions. However, most pathogen-host protein-protein interactions, which would be able to reveal much more infectious mechanisms between pathogen and host, are still up for further investigation. Considering a decent feature representation of pathogen-host proteinprotein interactions (PHPPI), currently there is not a well structured database for research purposes, not even for infection mechanism studies for different species of pathogens. In this paper, we will survey the PHPPI researches and construct a public PHPPI dataset by ourselves for future research. It results in an utterly big and imbalanced data set associated with high dimension and large quantity. Several machine learning methodologies are also discussed in this paper to imply possible analytics solutions in near future. This paper contributes to a new, yet challenging, research area in applying data analytic technologies in bioinformatics, by learning and predicting pathogen-host protein-protein interactions. Abstract-"Big Data" is immersed in many disciplines, including computer vision, economics, online resources, bioinformatics and so on. Increasing researches are conducted on data mining and machine learning for uncovering and predicting related domain knowledge. Protein-protein interaction is one of the main areas in bioinformatics as it is the basis of the biological functions. However, most pathogen-host proteinprotein interactions, which would be able to reveal much more infectious mechanisms between pathogen and host, are still up for further investigation. Considering a decent feature representation of pathogen-host protein-protein interactions (PHPPI), currently there is not a well structured database for research purposes, not even for infection mechanism studies for different species of pathogens. In this paper, we will survey the PHPPI researches and construct a public PHPPI dataset by ourselves for future research. It results in an utterly big and imbalanced data set associated with high dimension and large quantity. Several machine learning methodologies are also discussed in this paper to imply possible analytics solutions in near future. This paper contributes to a new, yet challenging, research area in applying data analytic technologies in bioinformatics, by learning and predicting pathogen-host proteinprotein interactions.

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Sequence-Based Prediction of Protein-Protein Interactions by Means of Rotation Forest and Autocorrelation Descriptor

Cited by 147 publications

References 69 publications

CombiMotif: A new algorithm for network motifs discovery in protein–protein interaction networks

CombiMotif: A new algorithm for network motifs discovery in protein–protein interaction networks

Prediction of Protein–Protein Interactions with Physicochemical Descriptors and Wavelet Transform via Random Forests

Towards Data Analytics of Pathogen-Host Protein-Protein Interaction: A Survey

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