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

2015 | OriginalPaper | Buchkapitel

A New Similarity Measure for Identification of Disease Genes

verfasst von : Pradipta Maji, Ekta Shah, Sushmita Paul

Erschienen in: Pattern Recognition and Machine Intelligence

Verlag: Springer International Publishing

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Abstract

One of the important problems in functional genomics is how to select the disease genes. In this regard, the paper presents a new similarity measure to compute the functional similarity between two genes. It is based on the information of protein-protein interaction networks. A new gene selection algorithm is introduced to identify disease genes, integrating judiciously the information of gene expression profiles and protein-protein interaction networks. The proposed algorithm selects a set of genes from microarray data as disease genes by maximizing the relevance and functional similarity of the selected genes. The performance of the proposed algorithm, along with a comparison with other related methods, is demonstrated on colorectal cancer data set.

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Vorheriges Kapitel PDP-RF: Protein Domain Boundary Prediction Using Random Forest Classifier
Nächstes Kapitel MaER: A New Ensemble Based Multiclass Classifier for Binding Activity Prediction of HLA Class II Proteins
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Metadaten
Titel
A New Similarity Measure for Identification of Disease Genes
verfasst von
Pradipta Maji
Ekta Shah
Sushmita Paul
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_43

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