Abstract
Protein-protein interactions (PPIs) play a key role in many cellular processes. Uncovering the PPIs and their function within the cell is a challenge of post-genomic biology and will improve our understanding of disease and help in the development of novel methods for disease diagnosis and forensics. The experimental methods currently used to identify PPIs are both time-consuming and expensive, and high throughput experimental results have shown both high false positive beside false negative information for protein interaction. These obstacles could be overcome by developing computational approaches to predict PPIs and validate the obtained experimental results. In this work, we will describe the recent advances in predicting protein-protein interaction from the following aspects: i) the benchmark dataset construction, ii) the sequence representation approaches, iii) the common machine learning algorithms, and iv) the cross-validation test methods and assessment metrics.
Keywords: Protein-protein interaction, prediction, dataset construction, sequence representation, machine learning, crossvalidation test.
Medicinal Chemistry
Title:Some Remarks on Prediction of Protein-Protein Interaction with Machine Learning
Volume: 11 Issue: 3
Author(s): Shao-Wu Zhang and Ze-Gang Wei
Affiliation:
Keywords: Protein-protein interaction, prediction, dataset construction, sequence representation, machine learning, crossvalidation test.
Abstract: Protein-protein interactions (PPIs) play a key role in many cellular processes. Uncovering the PPIs and their function within the cell is a challenge of post-genomic biology and will improve our understanding of disease and help in the development of novel methods for disease diagnosis and forensics. The experimental methods currently used to identify PPIs are both time-consuming and expensive, and high throughput experimental results have shown both high false positive beside false negative information for protein interaction. These obstacles could be overcome by developing computational approaches to predict PPIs and validate the obtained experimental results. In this work, we will describe the recent advances in predicting protein-protein interaction from the following aspects: i) the benchmark dataset construction, ii) the sequence representation approaches, iii) the common machine learning algorithms, and iv) the cross-validation test methods and assessment metrics.
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Cite this article as:
Zhang Shao-Wu and Wei Ze-Gang, Some Remarks on Prediction of Protein-Protein Interaction with Machine Learning, Medicinal Chemistry 2015; 11 (3) . https://dx.doi.org/10.2174/1573406411666141230095838
DOI https://dx.doi.org/10.2174/1573406411666141230095838 |
Print ISSN 1573-4064 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-6638 |
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Carbohydrates are the most essential organic molecules and are involved in the maintenance of various physiological and metabolic processes in living organisms. Carbohydrate-based compounds have come to the attention of researchers because of their significant contributions to biological functions, such as cell development and cell proliferation, connections between several cells, ...read more
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