作者机构：
[He, Tingting; Hsu, Ching-Fang; Zhang, Maoyuan] Cent China Normal Univ, Comp Sch, Wuhan 430079, Peoples R China.;[Harn, Lein] Univ Missouri, Dept Comp Sci Elect Engn, Kansas City, MO 64110 USA.
通讯机构：
[Hsu, Ching-Fang] C;Cent China Normal Univ, Comp Sch, Wuhan 430079, Peoples R China.
关键词：
Group key transfer protocol;secret sharing;LSSS based on Vandermonde matrix;wireless sensor networks
摘要：
Special designs are needed for cryptographic schemes in wireless sensor networks (WSNs). This is because sensor nodes are limited in memory storage and computational power. The existing group key transfer protocols for WSNs using classical secret sharing require that a t-degree interpolating polynomial be computed in order to encrypt and decrypt the secret group key. This approach is too computationally intensive. In this paper, we propose a new group key transfer protocol using a linear secret sharing scheme and factoring assumption. The proposed protocol can resist potential attacks and also significantly reduce the computation complexity of the system while maintaining low communication cost. Such a scheme is desirable for secure group communications in WSNs, where portable devices or sensors need to reduce their computation as much as possible due to battery power limitations.
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.
期刊：
International Conference on Information and Knowledge Management, Proceedings,2015年19-23-Oct-2015:1621-1630
作者机构：
[Shang, Yue; Hu, Xiaohua; Ding, Wanying] College of Computing and Informatics, Drexel University, Philadelphia, PA, United States;[He, Tingting; Yan, Rui] School of Computer, Central China Normal University, Wuhan, Hubei, China;[Guo, Lifan] TCL Research American, San Jose, CA, United States
期刊：
PROCEEDINGS 2015 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE,2015年:1177-1183 ISSN：2156-1125
通讯作者：
Yuan, Jie
作者机构：
[Jiang, Xingpeng; He, Tingting; Yuan, Jie] Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.;[Guo, Xiyue; Wang, Yan] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.
通讯机构：
[Yuan, Jie] C;Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.
会议名称：
IEEE International Conference on Bioinformatics and Biomedicine - Medical Informatics and Decision Making
会议时间：
NOV 09-12, 2015
会议地点：
Washington, DC
会议主办单位：
[Yuan, Jie;Jiang, Xingpeng;He, Tingting] Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.^[Wang, Yan;Guo, Xiyue] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.
会议论文集名称：
IEEE International Conference on Bioinformatics and Biomedicine-BIBM
关键词：
PPI network;Phenotype ontology;protein complexes;resolution-limit-free clustering algorithm
摘要：
Disease-causing genes prioritization is very important to understand disease mechanisms and biomedical applications, such as design of drugs. Previous studies have shown that promising candidate genes are mostly ranked according to their relatedness to known disease genes or closely related disease genes. Therefore, a dangling gene (isolated gene) with no edges in the network can not be effectively prioritized. These approaches tend to prioritize those genes that are highly connected in the PPI network while perform poorly when they are applied to loosely connected disease genes. To address these problems, we propose a new disease-causing genes prioritization method that based on network diffusion and rank concordance (NDRC). The method is evaluated by leave-one-out cross validation on 1931 diseases in which at least one gene is known to be involved, and it is able to rank the true causal gene first in 849 of all 2542 cases. The experimental results suggest that NDRC significantly outperforms other existing methods such as RWR, VAVIEN, DADA and PRINCE on identifying loosely connected disease genes and successfully put dangling genes as potential candidate disease genes. Furthermore, we apply NDRC method to study three representative diseases, Meckel syndrome 1, Protein C deficiency and Peroxisome biogenesis disorder 1A (Zellweger). Our study has also found that certain complex disease-causing genes can be divided into several modules that are closely associated with different disease phenotype.
作者：
Zhou, Guangyou*;He, Tingting（何婷婷）;Wu, Wensheng;Hu, Xiaohua Tony
作者机构：
[He, Tingting; Zhou, Guangyou; Hu, Xiaohua Tony] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.;[Wu, Wensheng] Univ Southern Calif, Comp Sci Dept, Los Angeles, CA USA.
会议名称：
1st International Workshop on Social Influence Analysis / 24th International Joint Conference on Artificial Intelligence (IJCAI)
会议时间：
JUL 25-31, 2015
会议地点：
Buenos Aires, ARGENTINA
会议主办单位：
[Zhou, Guangyou;He, Tingting;Hu, Xiaohua Tony] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.^[Wu, Wensheng] Univ Southern Calif, Comp Sci Dept, Los Angeles, CA USA.
摘要：
Sentiment classification aims to automatically predict sentiment polarity (e.g., positive or negative) of user generated sentiment data (e.g., reviews, blogs). In real applications, these user generated sentiment data can span so many different domains that it is difficult to manually label training data for all of them. Hence, this paper studies the problem of domain adaptation for sentiment classification where a system trained using labeled reviews from a source domain is deployed to classify sentiments of reviews in a different target domain. In this paper, we propose to link heterogeneous input features with pivots via joint non-negative matrix factorization. This is achieved by learning the domain-specific information from different domains into unified topics, with the help of pivots across all domains. We conduct experiments on a benchmark composed of reviews of 4 types of Amazon products. Experimental results show that our proposed approach significantly outperforms the baseline method, and achieves an accuracy which is competitive with the state-of-the-art methods for sentiment classification adaptation.
作者机构：
[He, Tingting; Zhou, Guangyou] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.;[Zhao, Jun] CASIA, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China.;[Wu, Wensheng] Univ Southern Calif, Comp Sci Dept, Los Angeles, CA 90089 USA.
会议名称：
1st International Workshop on Social Influence Analysis / 24th International Joint Conference on Artificial Intelligence (IJCAI)
会议时间：
JUL 25-31, 2015
会议地点：
Buenos Aires, ARGENTINA
会议主办单位：
[Zhou, Guangyou;He, Tingting] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.^[Zhao, Jun] CASIA, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China.^[Wu, Wensheng] Univ Southern Calif, Comp Sci Dept, Los Angeles, CA 90089 USA.
摘要：
Cross-lingual sentiment classification aims to automatically predict sentiment polarity (e.g., positive or negative) of data in a label-scarce target language by exploiting labeled data from a label-rich language. The fundamental challenge of cross-lingual learning stems from a lack of overlap between the feature spaces of the source language data and that of the target language data. To address this challenge, previous work in the literature mainly relies on the large amount of bilingual parallel corpora to bridge the language gap. In many real applications, however, it is often the case that we have some partial parallel data but it is an expensive and time-consuming job to acquire large amount of parallel data on different languages. In this paper, we propose a novel subspace learning framework by leveraging the partial parallel data for cross-lingual sentiment classification. The proposed approach is achieved by jointly learning the document-aligned review data and un-aligned data from the source language and the target language via a non-negative matrix factorization framework. We conduct a set of experiments with cross-lingual sentiment classification tasks on multilingual Amazon product reviews. Our experimental results demonstrate the efficacy of the proposed cross-lingual approach.
作者：
Wang, Yan*;Hu, Xiaohua;Jiang, Xingpeng（蒋兴鹏）;He, Tingting（何婷婷）;Yuan, Jie
期刊：
PROCEEDINGS 2015 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE,2015年:635-638 ISSN：2156-1125
通讯作者：
Wang, Yan
作者机构：
[Wang, Yan] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.;[Jiang, Xingpeng; He, Tingting; Hu, Xiaohua; Yuan, Jie] Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Peoples R China.;[Hu, Xiaohua] Drexel Univ, Coll Comp & Informat, Philadelphia, PA 19104 USA.
通讯机构：
[Wang, Yan] C;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
会议名称：
IEEE International Conference on Bioinformatics and Biomedicine - Medical Informatics and Decision Making
会议时间：
NOV 09-12, 2015
会议地点：
Washington, DC
会议主办单位：
[Wang, Yan] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.^[Hu, Xiaohua;Jiang, Xingpeng;He, Tingting;Yuan, Jie] Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Peoples R China.^[Hu, Xiaohua] Drexel Univ, Coll Comp & Informat, Philadelphia, PA 19104 USA.
会议论文集名称：
IEEE International Conference on Bioinformatics and Biomedicine-BIBM
关键词：
Coordinate descent;Microbial interactions;Microbiome;Time series analysis;Vector autoregression model
期刊：
INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS,2015年13(4):378-394 ISSN：1748-5673
通讯作者：
He, Tingting
作者机构：
[Wang, Yan] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.;[Jiang, Xingpeng; He, Tingting; Shen, Xianjun; Yuan, Jie] Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Peoples R China.
通讯机构：
[He, Tingting] C;Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Peoples R China.
摘要：
In this paper, we develop a novel regularisation method for MVAR via weighted fusion which considers the correlation among variables. In theory, we discuss the grouping effect of weighted fusion regularisation for linear models. By virtue of the probability method, we show that coefficients corresponding to highly correlated predictors have small differences. A quantitative estimate for such small differences is given regardless of the coefficients signs. The estimate is also improved when consider empirical approximation error if the model fit the data well. We then apply the proposed model on several time series data sets especially a time series dataset of human gut microbiomes. The experimental results indicate that the new approach has better performance than several other VAR-based models and we also demonstrate its capability of extracting relevant microbial interactions.
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.
期刊：
PROCEEDINGS 2015 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE,2015年:1133-1140 ISSN：2156-1125
通讯作者：
Guo, Xiyue
作者机构：
[Guo, Xiyue] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.;[He, Tingting; Yuan, Jie] Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.;[Guo, Xiyue] Xingyi Normal Univ Nationalities, Sch Informat Technol, Xingyi, Peoples R China.
通讯机构：
[Guo, Xiyue] C;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.
会议名称：
IEEE International Conference on Bioinformatics and Biomedicine - Medical Informatics and Decision Making
会议时间：
NOV 09-12, 2015
会议地点：
Washington, DC
会议主办单位：
[Guo, Xiyue] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.^[He, Tingting;Yuan, Jie] Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.^[Guo, Xiyue] Xingyi Normal Univ Nationalities, Sch Informat Technol, Xingyi, Peoples R China.
会议论文集名称：
IEEE International Conference on Bioinformatics and Biomedicine-BIBM
关键词：
PPI Extraction;Weakly Supervised;Dictionary Construction;Slot-filling;Rule Learning
作者机构：
[He, Tingting; Tu, Xinhui; Zhou, Guangyou; Zhou, Yin; Guo, Xiyue] Cent China Normal Univ, Sch Comp, Wuhan 430079, Peoples R China.;[Zhou, Guangyou] Cent China Normal Univ, Sch Comp, 152 Luoyu Rd, Wuhan 430079, Peoples R China.
通讯机构：
[Zhou, Guangyou] C;Cent China Normal Univ, Sch Comp, 152 Luoyu Rd, Wuhan 430079, Peoples R China.
关键词：
Sentiment classification;Cross-domain;Topical correspondence transfer
摘要：
Sentiment classification aims to automatically predict sentiment polarity (e.g., positive or negative) of user generated sentiment data (e.g., reviews, blogs). In real applications, these users generated sentiment data can span so many different domains that it is difficult to manually label training data for all of them. In this article, we develop a general solution to cross-domain sentiment classification when we do not have any labeled data in a target domain but have some labeled data in a source domain. To bridge the gap between domains, we propose a novel algorithm, called topical correspondence transfer (TCT). This is achieved by learning the domain-specific information from different domains into unified topics, with the help of shared topics across all domains. In this way, the topical correspondences behind the shared topics can be used as a bridge to reduce the gap between domains. We conduct experiments on a benchmark composed of reviews of 4 types of Amazon products. Experimental results show that our proposed TCT significantly outperforms the baseline method, and achieves an accuracy which is competitive with the state-of-the-art methods for cross-domain sentiment classification.
