Functional modules detection based on bat algorithm in protein-protein interaction networks

doi:10.3785/j.issn.1008-973X.2019.08.021

Journal of ZheJiang University (Engineering Science)

2019, Vol. 53

Issue (8): 1618-1629 DOI: 10.3785/j.issn.1008-973X.2019.08.021

Chemical Engineering, Biological Engineering

Functional modules detection based on bat algorithm in protein-protein interaction networks

Jia-hao XU(

),Jun-zhong JI*(

),Cui-cui YANG

Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Beijing University of Technology, Beijing 100124, China

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Abstract

The bat algorithm was used to detect the functional modules in protein-protein interaction networks (PPINs), in order to get better protein functional modules and reveal the function of proteins. The position of each bat individual represents a candidate functional module partition. Each protein node in PPIN and all its neighbor nodes form an ordered adjacency list and the population is initialized by random walk coding method in the ordered adjacency list. Four kinds of optimization mechanisms, namely directional local disturbance, random disturbance, adaptive variation based on distance and frequency, natural selection, are designed for the random optimization of solutions in the process of population optimization. The comparison experiments of the proposed algorithm and six classical algorithms were conducted on five yeast PPIN datasets having different scales. Results showed that many functional modules detected by the proposed method matched the standard modules and the evaluation indexes including coverage, recall, sensitivity, positive predictive value and accuracy were outstanding, which verified the validity of the proposed method.

Key words： protein-protein interaction network (PPIN) functional module detection bat algorithm disturbance adaptive variation natural selection

Received: 11 July 2018 Published: 13 August 2019

CLC:	Q 811
	TP 301

Corresponding Authors: Jun-zhong JI E-mail: xjh8239@163.com;jjz01@bjut.edu.cn

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Cite this article:

Jia-hao XU,Jun-zhong JI,Cui-cui YANG. Functional modules detection based on bat algorithm in protein-protein interaction networks. Journal of ZheJiang University (Engineering Science), 2019, 53(8): 1618-1629.

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http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2019.08.021 OR http://www.zjujournals.com/eng/Y2019/V53/I8/1618

基于蝙蝠算法的蛋白质网络功能模块检测

为了得到更好的蛋白质功能模块，揭示蛋白质的功能，利用蝙蝠算法对蛋白质相互作用网络(PPINs)进行功能模块检测. 每个蝙蝠个体所在的位置代表一种候选的功能模块划分，将PPIN中每个蛋白质节点与其所有邻居节点组成邻居有序表，采用在邻居有序表中随机游走的编码方式进行种群的初始化；在种群优化过程中，设计定向局部扰动、随机扰动、基于距离和频率的自适应变异、自然选择4种寻优机制来进行解的随机优化. 在5个不同规模的酵母菌PPIN数据集上，将所提出方法与6种经典算法进行对比实验. 结果表明，所提出方法检测到的功能模块中有较多模块与标准模块相匹配，并且所提出算法在覆盖率、召回率、灵敏度、正的预测率、准确度评价指标上均表现突出，验证了所提出方法的有效性.

关键词： 蛋白质相互作用网络(PPIN), 功能模块检测, 蝙蝠算法, 扰动, 自适应变异, 自然选择

Fig.1 Example of protein-protein interaction network

Fig.2 Ordered adjacency list in initialization of bat individual

Fig.3 Encoding of bat individual position in BA-FMD algorithm

Fig.4 Functional modules after decoding of bat individual position in BA-FMD algorithm

Fig.5 Schematic diagram of directional local disturbance operation of bat individual in BA-FMD algorithm

Fig.6 Schematic diagram of random disturbance operation of bat individual in BA-FMD algorithm

Tab.1 Number of nodes and edges for Gavin, DIPcore, Krogan, Collins and BioGRID datasets

Tab.2 Connection similarity threshold and filtering threshold on different datasets in BA-FMD algorithm

Fig.7 Optimal individual fitness of BA-FMD algorithm for convergence under different population sizes

Fig.8 Values of evaluation indexes under different combined thresholds in BA-FMD algorithm

Tab.3 Experimental results of seven algorithms on five datasets

Fig.9 Comparison of different evaluation indexes of seven algorithms on five datasets


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