Gene Ontology-driven inference of protein–protein interactions using inducers

Maetschke, Stefan; Simonsen, Martin; Davis, Melissa J.; Ragan, Mark A.

doi:10.1093/bioinformatics/btr610

Cited by 80 publications

(88 citation statements)

References 36 publications

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“…The annotations of these three aspects of genes or gene products are provided in terms of GO terms in the GOA database18. Recently GO terms have been successfully used as features to predict protein-protein interactions1920212223. There are two effective approaches to exploit GO terms for representing protein pairs.…”

Section: Methodsmentioning

confidence: 99%

“…There are two effective approaches to exploit GO terms for representing protein pairs. One approach is to exploit the shared GO terms between two proteins and construct explicit binary feature vectors as the inputs of machine learning methods20212223, and the other approach is to measure the similarity between GO terms in GO DAG (directed acyclic graph) and construct an implicit kernel matrix as the input of kernel methods19.…”

Section: Methodsmentioning

confidence: 99%

“…One method is to directly use the GO terms extracted from the GOA database alone212223, and the other method is to incorporate the ancestor GO terms of each GO term concerned20. Incorporation of ancestor GO terms surely adds useful information to the training data and thus improves the model performance.…”

Section: Methodsmentioning

confidence: 99%

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Multi-label ℓ2-regularized logistic regression for predicting activation/inhibition relationships in human protein-protein interaction networks

Mei

Zhang

2016

Sci Rep

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Protein-protein interaction (PPI) networks are naturally viewed as infrastructure to infer signalling pathways. The descriptors of signal events between two interacting proteins such as upstream/downstream signal flow, activation/inhibition relationship and protein modification are indispensable for inferring signalling pathways from PPI networks. However, such descriptors are not available in most cases as most PPI networks are seldom semantically annotated. In this work, we extend ℓ2-regularized logistic regression to the scenario of multi-label learning for predicting the activation/inhibition relationships in human PPI networks. The phenomenon that both activation and inhibition relationships exist between two interacting proteins is computationally modelled by multi-label learning framework. The problem of GO (gene ontology) sparsity is tackled by introducing the homolog knowledge as independent homolog instances. ℓ2-regularized logistic regression is accordingly adopted here to penalize the homolog noise and to reduce the computational complexity of the double-sized training data. Computational results show that the proposed method achieves satisfactory multi-label learning performance and outperforms the existing phenotype correlation method on the experimental data of Drosophila melanogaster. Several predictions have been validated against recent literature. The predicted activation/inhibition relationships in human PPI networks are provided in the supplementary file for further biomedical research.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Multi-label ℓ2-regularized logistic regression for predicting activation/inhibition relationships in human protein-protein interaction networks

Mei

Zhang

2016

Sci Rep

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“…In general, combining weak evidence for membership in an interactome can result in more successful predictors. Such approaches include Bayesian classifiers (18, 81, 112, 118, 194, 195), decision tree approaches (169) support vector machines (SVMs) (34, 101, 140), random forest classifiers (26, 27, 45), and a host of other techniques (13, 20, 74, 122, 126, 187). …”

Section: Building Interactomesmentioning

confidence: 99%

Structural Bioinformatics of the Interactome

Petrey

2014

Annu. Rev. Biophys.

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The last decade has seen a dramatic expansion in the number and range of techniques available to obtain genome-wide information, and to analyze this information so as to infer both the function of individual molecules and how they interact to modulate the behavior of biological systems. Here we review these techniques, focusing on the construction of physical protein-protein interaction networks, and highlighting approaches that incorporate protein structure which is becoming an increasingly important component of systems-level computational techniques. We also discuss how network analyses are being applied to enhance the basic understanding of biological systems and their disregulation, and how they are being applied in drug development.

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“…To improve the model performance, most of the existing methods generally leverage a catalog of biological feature information, e.g. binding motif, gene expression profile, gene co-expression, gene ontology, sequence k -mer, post-translational modification, protein structural information and PPI network topology1213142930, etc. Among these types of feature information, the sequence information of protein achieves relatively moderate discriminative ability2223, though less expensive to obtain.…”

mentioning

confidence: 99%

A novel one-class SVM based negative data sampling method for reconstructing proteome-wide HTLV-human protein interaction networks

Mei

Zhu

2015

Sci Rep

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Protein-protein interaction (PPI) prediction is generally treated as a problem of binary classification wherein negative data sampling is still an open problem to be addressed. The commonly used random sampling is prone to yield less representative negative data with considerable false negatives. Meanwhile rational constraints are seldom exerted on model selection to reduce the risk of false positive predictions for most of the existing computational methods. In this work, we propose a novel negative data sampling method based on one-class SVM (support vector machine, SVM) to predict proteome-wide protein interactions between HTLV retrovirus and Homo sapiens, wherein one-class SVM is used to choose reliable and representative negative data, and two-class SVM is used to yield proteome-wide outcomes as predictive feedback for rational model selection. Computational results suggest that one-class SVM is more suited to be used as negative data sampling method than two-class PPI predictor, and the predictive feedback constrained model selection helps to yield a rational predictive model that reduces the risk of false positive predictions. Some predictions have been validated by the recent literature. Lastly, gene ontology based clustering of the predicted PPI networks is conducted to provide valuable cues for the pathogenesis of HTLV retrovirus.

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Gene Ontology-driven inference of protein–protein interactions using inducers

Abstract: m.ragan@uq.edu.au.

Cited by 80 publications

References 36 publications

Multi-label ℓ2-regularized logistic regression for predicting activation/inhibition relationships in human protein-protein interaction networks

Multi-label ℓ2-regularized logistic regression for predicting activation/inhibition relationships in human protein-protein interaction networks

Structural Bioinformatics of the Interactome

A novel one-class SVM based negative data sampling method for reconstructing proteome-wide HTLV-human protein interaction networks

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