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Neural Computing and Applications

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30-07-2018 | S.I. : Emergence in Human-like Intelligence towards Cyber-Physical Systems

A novel temporal protein complexes identification framework based on density–distance and heuristic algorithm

Authors: Dan Xie, Yang Yi, Jin Zhou, Xiaodong Li, Huikun Wu

Published in: Neural Computing and Applications | Issue 9/2019

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Abstract

The construction of dynamic protein–protein interaction networks is affected by cell tissue and its biological function, and the identification of protein complexes is important for understanding biological functions. This paper presents a new method named density–distance and heuristic for identifying temporal protein complexes. First, the gene expression data of time course are integrated into the static protein interaction data and a set of time-ordered networks are obtained. Then, the network is integrated with the gene information to calculate the distance between proteins in the protein–protein interaction network. Based on this distance, we have formed a number of clusters and selected the furthest cluster from the other cluster centers as the initial cluster to ensure that nodes with clusters are closest to each other. Finally, a heuristic algorithm is introduced, and the initial cluster is updated in two ways. The experimental results show that the proposed method has better performance compared with the commonly used algorithms; meanwhile, this method has a strong biological significance.

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Title: A novel temporal protein complexes identification framework based on density–distance and heuristic algorithm
Authors: Dan Xie
Yang Yi
Jin Zhou
Xiaodong Li
Huikun Wu
Publication date: 30-07-2018
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 9/2019
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-018-3660-5

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