Supervised Statistical and Machine Learning Approaches to Inferring Pairwise and Module-Based Protein Interaction Networks

Browne, Fiona; Wang, Haiying; Zheng, Huiru; Azuaje, Francisco

doi:10.1109/bibe.2007.4375748

Cited by 12 publications

(15 citation statements)

References 5 publications

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“…Previous work by [ 13 ] evaluated three classification techniques Naive Bayesian (NB), Multi-Layer Perceptron (MLP) and K-Nearest Neighbour (KNN) for the predictive task of inferring pair-wise and module-based PPI interaction networks in S. cerevisiae . Seven functional genomic data ranging from co-expression to essentiality were integrated using the classification techniques.…”

Section: Resultsmentioning

confidence: 99%

“…From Figure 4 it is observed that the NB and MLP obtain a marginally higher AUC values compared to the KNN classifier. Additional information on the prediction of pair-wise and module-based PPI networks can be found in the study [ 13 ].…”

Section: Resultsmentioning

confidence: 99%

“…The automatic construction of these reference datasets removes programming required thereby simplifying and saving time for researchers. Recent research by [ 2 , 3 , 13 ] demonstrated that the generation of reference datasets are critical for the verification of computationally-inferred PPI networks. A study by [ 13 ] implemented reference datasets constructed using GRIP to demonstrate that supervised statistical and machine learning techniques can be successfully applied to pair-wise and module-based interaction prediction.…”

Section: Discussionmentioning

confidence: 99%

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GRIP: A web-based system for constructing Gold Standard datasets for protein-protein interaction prediction

Browne

Wang

Zheng

et al. 2009

Source Code Biol Med

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GRIP is developed to retrieve information on protein complex, cellular localisation, and physical and genetic interactions in Saccharomyces cerevisiae. Manual construction of reference datasets can be a time consuming process requiring programming knowledge. GRIP simplifies and speeds up this process by allowing users to automatically construct reference datasets. GRIP is free to access at http://rosalind.infj.ulst.ac.uk/GRIP/.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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GRIP: A web-based system for constructing Gold Standard datasets for protein-protein interaction prediction

Browne

Wang

Zheng

et al. 2009

Source Code Biol Med

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“…Recent research by [10,11] demonstrated that the generation of reference datasets are critical for the verification of computationallyinferred PPI networks. A study by [74] implemented reference datasets constructed using GRIP to demonstrate that supervised statistical and machine learning techniques can be successfully applied to PW and MB interaction prediction.…”

Section: Gold Standardsmentioning

confidence: 99%

“…Statistical and machine learning techniques can be applied in the computational prediction of PPI [10,11,74]. These techniques are required for the integration of heterogeneous features and the inference of PPI networks.…”

Section: Conclusion and Future Trendsmentioning

confidence: 99%

From Experimental Approaches to Computational Techniques: A Review on the Prediction of Protein-Protein Interactions

Browne

Zheng

Wang

et al. 2010

Advances in Artificial Intelligence

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A crucial step towards understanding the properties of cellular systems in organisms is to map their network of protein-protein interactions (PPIs) on a proteomic-wide scale completely and as accurately as possible. Uncovering the diverse function of proteins and their interactions within the cell may improve our understanding of disease and provide a basis for the development of novel therapeutic approaches. The development of large-scale high-throughput experiments has resulted in the production of a large volume of data which has aided in the uncovering of PPIs. However, these data are often erroneous and limited in interactome coverage. Therefore, additional experimental and computational methods are required to accelerate the discovery of PPIs. This paper provides a review on the prediction of PPIs addressing key prediction principles and highlighting the common experimental and computational techniques currently employed to infer PPI networks along with relevant studies in the area.

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DeepPPI: Boosting Prediction of Protein–Protein Interactions with Deep Neural Networks

Sun

et al. 2017

J. Chem. Inf. Model.

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The complex language of eukaryotic gene expression remains incompletely understood. Despite the importance suggested by many proteins variants statistically associated with human disease, nearly all such variants have unknown mechanisms, for example, protein-protein interactions (PPIs). In this study, we address this challenge using a recent machine learning advance-deep neural networks (DNNs). We aim at improving the performance of PPIs prediction and propose a method called DeepPPI (Deep neural networks for Protein-Protein Interactions prediction), which employs deep neural networks to learn effectively the representations of proteins from common protein descriptors. The experimental results indicate that DeepPPI achieves superior performance on the test data set with an Accuracy of 92.50%, Precision of 94.38%, Recall of 90.56%, Specificity of 94.49%, Matthews Correlation Coefficient of 85.08% and Area Under the Curve of 97.43%, respectively. Extensive experiments show that DeepPPI can learn useful features of proteins pairs by a layer-wise abstraction, and thus achieves better prediction performance than existing methods. The source code of our approach can be available via http://ailab.ahu.edu.cn:8087/DeepPPI/index.html .

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Supervised Statistical and Machine Learning Approaches to Inferring Pairwise and Module-Based Protein Interaction Networks

Cited by 12 publications

References 5 publications

GRIP: A web-based system for constructing Gold Standard datasets for protein-protein interaction prediction

GRIP: A web-based system for constructing Gold Standard datasets for protein-protein interaction prediction

From Experimental Approaches to Computational Techniques: A Review on the Prediction of Protein-Protein Interactions

DeepPPI: Boosting Prediction of Protein–Protein Interactions with Deep Neural Networks

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