期刊：
International Journal of Wireless and Mobile Computing,2016年11(1):18-23 ISSN：1741-1084
通讯作者：
Shen, Xianjun(xjshen@mail.ccnu.edu.cn)
作者机构：
[Hu, Wenjie] College of Information Engineering, Xianning Vocational Technical College, Xianning, Hubei, China;[Yang, Jincai; Chen, Yao; Shen, Xianjun] School of Computer, Central China Normal University, Wuhan, Hubei, China;Collaborative & Innovative Center for Educational Technology, Central China Normal University, Wuhan, Hubei, China;[Shen, Xianjun] Collaborative and Innovative Center for Educational Technology, Central China Normal University, Wuhan, Hubei, China
通讯机构：
School of Computer, Central China Normal University, Wuhan, Hubei, China
摘要：
Aiming at the critical drawbacks of low sampling rate and less accuracy in Monte Carlo Localisation (MCL) algorithm, a novel mobile nodes localisation algorithm based on the hill climbing optimisation strategy is proposed, namely HCPSO-MCL (Hill Climbing Particle Swarm Optimisation-MCL). The HCPSO-MCL algorithm combines the hill climbing strategy and particle swarm optimisation to correct the location estimated by the MCL algorithm, which results in effective implementation and accurate positioning of the mobile nodes. The experimental results indicate that the HCPSO-MCL algorithm improves the positioning accuracy greatly compared to the MCL algorithm and that it has a faster position velocity than the PSO-MCL algorithm.
作者机构：
[Jiang, Xingpeng; Yang, Jincai; He, Tingting; Shen, Xianjun; Hu, Xiaohua; Yi, Li; Zhao, Yanli] Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.;[Hu, Xiaohua] Drexel Univ, Coll Comp & Informat, Philadelphia, PA 19104 USA.
通讯机构：
[Yang, Jincai] C;Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.
关键词：
*Clustering coefficient;*Neighbor affinity;*Temporal protein complex;*Time course protein interaction networks
摘要：
Detection of temporal protein complexes would be a great aid in furthering our knowledge of the dynamic features and molecular mechanism in cell life activities. Most existing clustering algorithms for discovering protein complexes are based on static protein interaction networks in which the inherent dynamics are often overlooked. We propose a novel algorithm DPC-NADPIN (Discovering Protein Complexes based on Neighbor Affinity and Dynamic Protein Interaction Network) to identify temporal protein complexes from the time course protein interaction networks. Inspired by the idea of that the tighter a protein’s neighbors inside a module connect, the greater the possibility that the protein belongs to the module, DPC-NADPIN algorithm first chooses each of the proteins with high clustering coefficient and its neighbors to consolidate into an initial cluster, and then the initial cluster becomes a protein complex by appending its neighbor proteins according to the relationship between the affinity among neighbors inside the cluster and that outside the cluster. In our experiments, DPC-NADPIN algorithm is proved to be reasonable and it has better performance on discovering protein complexes than the following state-of-the-art algorithms: Hunter, MCODE, CFinder, SPICI, and ClusterONE; Meanwhile, it obtains many protein complexes with strong biological significance, which provide helpful biological knowledge to the related researchers. Moreover, we find that proteins are assembled coordinately to form protein complexes with characteristics of temporality and spatiality, thereby performing specific biological functions.
作者机构：
[Jiang, Xingpeng; Yang, Jincai; He, Tingting; Shen, Xianjun; Hu, Xiaohua; Yi, Li] Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.;[Hu, Xiaohua] Drexel Univ, Coll Comp & Informat, Philadelphia, PA 19104 USA.
通讯机构：
[Yang, Jincai] C;Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.
关键词：
Protein complexes;Protein interactions;Protein interaction networks;Gene expression;Algorithms;Molecular evolution;Protein expression;Protein metabolism
摘要：
The identification of temporal protein complexes would make great contribution to our knowledge of the dynamic organization characteristics in protein interaction networks (PINs). Recent studies have focused on integrating gene expression data into static PIN to construct dynamic PIN which reveals the dynamic evolutionary procedure of protein interactions, but they fail in practice for recognizing the active time points of proteins with low or high expression levels. We construct a Time-Evolving PIN (TEPIN) with a novel method called Deviation Degree, which is designed to identify the active time points of proteins based on the deviation degree of their own expression values. Owing to the differences between protein interactions, moreover, we weight TEPIN with connected affinity and gene co-expression to quantify the degree of these interactions. To validate the efficiencies of our methods, ClusterONE, CAMSE and MCL algorithms are applied on the TEPIN, DPIN (a dynamic PIN constructed with state-of-the-art three-sigma method) and SPIN (the original static PIN) to detect temporal protein complexes. Each algorithm on our TEPIN outperforms that on other networks in terms of match degree, sensitivity, specificity, F-measure and function enrichment etc. In conclusion, our Deviation Degree method successfully eliminates the disadvantages which exist in the previous state-of-the-art dynamic PIN construction methods. Moreover, the biological nature of protein interactions can be well described in our weighted network. Weighted TEPIN is a useful approach for detecting temporal protein complexes and revealing the dynamic protein assembly process for cellular organization.
摘要：
Protein complexes comprising of interacting proteins in protein-protein interaction network (PPI network) play a central role in driving biological processes within cells. Recently, more and more swarm intelligence based algorithms to detect protein complexes have been emerging, which have become the research hotspot in proteomics field. In this paper, we propose a novel algorithm for identifying protein complexes based on brainstorming strategy (IPC-BSS), which is integrated into the main idea of swarm intelligence optimization and the improved K-means algorithm. Distance between the nodes in PPI network is defined by combining the network topology and gene ontology (GO) information. Inspired by human brainstorming process, IPC-BSS algorithm firstly selects the clustering center nodes, and then they are separately consolidated with the other nodes with short distance to form initial clusters. Finally, we put forward two ways of updating the initial clusters to search optimal results. Experimental results show that our IPC-BSS algorithm outperforms the other classic algorithms on yeast and human PPI networks, and it obtains many predicted protein complexes with biological significance.
摘要：
Abstract: Compound sentences, connecting sentences and paragraph, play an important role in Chinese in-formation processing. The research of relation word recognition is regarded as the breakthrough point for the research of compound sentences. Based on the dependency relationship in Chinese syntax and the characteristics and regularity of relation words and their collocations, this paper recognizes as well as extracts relation words automatically and established the relationship word collocation corpus with CCCS. The collocation corpus records the status of the match and use of various relation words in compound sentences, which will be advantageous to analyze the matching rule of the word collocation rule, and obtain rules for automatic relationship recognition, ultimately lay the foundation for the more accurate identification of the relation word.#@#@#摘要: 复句作为联系句子与篇章的桥梁，在中文信息处理中具有重要的地位，关系词的识别研究是复句研究的切入点。本文基于汉语依存句法、关系词及搭配的特征与规律、辅以关系词本体知识库，自动识别并提取关系词，建立了关系词搭配语料库。该关系词搭配库记录了各种关系词在复句中使用与搭配的状态，将有利于分析与统计关系词搭配的规律，从中获取用于关系词自动识别的规则，为关系词更准确的识别打下基础。
期刊：
INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS,2015年11(4):458-473 ISSN：1748-5673
通讯作者：
Shen, Xianjun
作者机构：
[Yang, Jincai; He, Tingting; Shen, Xianjun; Yi, Yang; Zhao, Yanli; Li, Yanan] Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.
通讯机构：
[Shen, Xianjun] C;Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.
关键词：
protein complexes;best neighbour node;modularity increment;protein-protein interactions network
摘要：
In order to overcome the limitations of global modularity and the deficiency of local modularity, we propose a hybrid modularity measure Local-Global Quantification (LGQ) which considers global modularity and local modularity together. LGQ adopts a suitable module feature adjustable parameter to control the balance of global detecting capability and local search capability in Protein-Protein Interactions (PPI) Network. Furthermore, we develop a new protein complex mining algorithm called Best Neighbour and Local-Global Quantification (BN-LGQ) which integrates the best neighbour node and modularity increment. BN-LGQ expands the protein complex by fast searching the best neighbour node of the current cluster and by calculating the modularity increment as a metric to determine whether the best neighbour node can join the current cluster. The experimental results show BN-LGQ performs a better accuracy on predicting protein complexes and has a higher match with the reference protein complexes than MCL and MCODE algorithms. Moreover, BN-LGQ can effectively discover protein complexes with better biological significance in the PPI network.
