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Logic Operation Model of the Complementer Based on Two-Domain DNA Strand Displacement Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Predicting Essential Proteins Based on Gene Expression Data, Subcellular Localization and PPI Data

Authors : Xiujuan Lei, Siguo Wang, Linqiang Pan

Published in: Bio-inspired Computing: Theories and Applications

Publisher: Springer Singapore

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Abstract

Predicting essential proteins is indispensable for understanding the minimal requirements of cellular survival and development. In recent years, many methods combined with the topological features of PPI networks have been proposed. However, most of these approaches ignored the intrinsic characteristics of biological attributes. This paper integrates Gene expression data, Subcellular localization and PPI networks to identify essential proteins, named GSP. We use local average connectivity and edge clustering coefficient unite with gene expression data to measure centralities of nodes. Compared with non-essential proteins, essential proteins appear more frequently in some subcellular localizations such as Nucleus and considering that different compartments play different roles, thus we integrate subcellular localization information to identify essential proteins. The computational experiment results on the yeast PPI networks show that the proposed method GSP outperforms other state-of-art methods including DC, EC, IC, SC, NC, LAC, PeC, WDC and UDoNC.

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Metadata
Title
Predicting Essential Proteins Based on Gene Expression Data, Subcellular Localization and PPI Data
Authors
Xiujuan Lei
Siguo Wang
Linqiang Pan
Copyright Year
2017
Publisher
Springer Singapore
Book
Bio-inspired Computing: Theories and Applications

Print ISBN: 978-981-10-7178-2

Electronic ISBN: 978-981-10-7179-9

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-981-10-7179-9_8

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