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Erschienen in:
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2017 | OriginalPaper | Buchkapitel

Protein Features Identification for Machine Learning-Based Prediction of Protein-Protein Interactions

verfasst von : Khalid Raza

Erschienen in: Information, Communication and Computing Technology

Verlag: Springer Singapore

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Abstract

The long awaited challenge of post-genomic era and systems biology research is computational prediction of protein-protein interactions (PPIs) that ultimately lead to protein functions prediction. The important research questions is how protein complexes with known sequence and structure be used to identify and classify protein binding sites, and how to infer knowledge from these classification such as predicting PPIs of proteins with unknown sequence and structure. Several machine learning techniques have been applied for the prediction of PPIs, but the accuracy of their prediction wholly depends on the number of features being used for training. In this paper, we have performed a survey of protein features used for the prediction of PPIs. The open research challenges and opportunities in the area have also been discussed.

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Vorheriges Kapitel A Hybridized Evolutionary Algorithm for Bi-objective Bi-dimensional Bin-packing Problem
Nächstes Kapitel Load Balancing Task Scheduling Based on Variants of Genetic Algorithms: Review Paper
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Metadaten
Titel
Protein Features Identification for Machine Learning-Based Prediction of Protein-Protein Interactions
verfasst von
Khalid Raza
Copyright-Jahr
2017
Verlag
Springer Singapore
Buch
Information, Communication and Computing Technology

Print ISBN: 978-981-10-6543-9

Electronic ISBN: 978-981-10-6544-6

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-981-10-6544-6_28