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Erschienen in:
Recent Advances in Recommender Systems and Future Directions Kapitel lesen Erstes Kapitel lesen

2015 | OriginalPaper | Buchkapitel

PDP-RF: Protein Domain Boundary Prediction Using Random Forest Classifier

verfasst von : Piyali Chatterjee, Subhadip Basu, Julian Zubek, Mahantapas Kundu, Mita Nasipuri, Dariusz Plewczynski

Erschienen in: Pattern Recognition and Machine Intelligence

Verlag: Springer International Publishing

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Abstract

The Domain Boundary Prediction is a crucial task for functional classification of proteins, homology-based protein structure prediction and for high-throughput structural genomics. Each amino acid is represented using a set of physico-chemical properties. Random Forest Classifier is explored for accurate prediction of domain regions by training on the curated dataset obtained from CATH database. The software is tested on proteins of CASP-6, CASP-8, CASP-9 and CASP-10 targets in order to evaluate its prediction accuracy using three fold cross validation experiments. Finally, a consensus approach is used to combine results of the classifiers obtained through the cross-validation experiments. The average recall and precision scores achieved by the developed consensus based Random Forest classifiers (PDP-RF) are 0.98 and 0.88 respectively for prediction of CASP targets. The overall accuracy and F-scores of the PDP-RF are observed as 0.87 and 0.91 respectively.

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Vorheriges Kapitel Global Optimization of Exact Association Rules Relative to Coverage
Nächstes Kapitel A New Similarity Measure for Identification of Disease Genes
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Metadaten
Titel
PDP-RF: Protein Domain Boundary Prediction Using Random Forest Classifier
verfasst von
Piyali Chatterjee
Subhadip Basu
Julian Zubek
Mahantapas Kundu
Mita Nasipuri
Dariusz Plewczynski
Copyright-Jahr
2015
Buch
Pattern Recognition and Machine Intelligence

Print ISBN: 978-3-319-19940-5

Electronic ISBN: 978-3-319-19941-2

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-3-319-19941-2_42

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