期刊：
Information Sciences,2021年550:27-40 ISSN：0020-0255
通讯作者：
Zhao, Weizhong;Yang, Jincai
作者机构：
[Yang, Jincai; He, Tingting; Zhang, Jinyong; Zhao, Weizhong; Zhao, WZ] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan 430079, Hubei, Peoples R China.;[Yang, Jincai; He, Tingting; Zhang, Jinyong; Zhao, Weizhong; Zhao, WZ] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.;[He, Tingting; Zhao, Weizhong] Cent China Normal Univ, Natl Language Resources Monitoring & Res Ctr Netw, Wuhan 430079, Hubei, Peoples R China.;[Zhao, Weizhong] Guilin Univ Elect Technol, Guangxi Key Lab Trusted Software, Guilin 541004, Peoples R China.;[Zhao, Weizhong] Guangxi Normal Univ, Guangxi Key Lab Multisource Informat Min & Secur, Guilin 541004, Peoples R China.
通讯机构：
[Zhao, WZ; Yang, JC; Zhao, Weizhong] C;[Zhao, Weizhong] G;Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan 430079, Hubei, Peoples R China.;Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.;Cent China Normal Univ, Natl Language Resources Monitoring & Res Ctr Netw, Wuhan 430079, Hubei, Peoples R China.
摘要：
With the rapid development of information technology, the amount of textual data generated in biomedical field becomes larger and larger. Biomedical event extraction, which is a fundamental information extraction task, has gained a growing interest in biomedical community. Although researchers have proposed various approaches to this task, the performance is still undesirable since previous approaches fail to model biomedical documents effectively. In this paper, we propose an end-to-end framework for document-level joint biomedical event extraction. To better capture the complex relationships among contexts in biomedical documents, a two-level modeling approach is introduced for biomedical documents. More specifically, the dependency-based GCN and hypergraph are used to model local context and global context in each biomedical document, respectively. In addition, a fine-grained interaction mechanism is proposed to model effectively the interaction between local and global contexts to learn better contextualized representations for biomedical event extraction. Comprehensive experiments on two widely used datasets are conducted and the results demonstrate the effectiveness of the proposed framework over state-of-the-art methods. (C) 2020 Elsevier Inc. All rights reserved.
摘要：
As a prerequisite step in biomedical event extraction, event trigger identification has attracted growing attention in biomedical research. Existing approaches to biomedical event trigger identification have two major drawbacks: (1) each sentence in a biomedical document is handled separately, which ignores the global context; (2) they fail to treat the issue of imbalanced class which is induced by the sparseness of event triggers in biomedical documents. To improve the performance of biomedical event trigger identification, we propose a deep neural network-based framework which addresses effectively the two mentioned challenges accordingly. Specifically, the syntactic dependency tree and hierarchical attention mechanism are utilised to model both local and global contexts. Moreover, we propose an adaptive cost learning method to address the class imbalance issue in biomedical event trigger identification. Extensive experiments are conducted on two real-world data sets, and the results demonstrate the effectiveness of the proposed framework.
期刊：
Frontiers in Genetics,2020年10:501186 ISSN：1664-8021
通讯作者：
Shen, Xianjun
作者机构：
[Yang, Jincai; Shen, Xianjun; Yu, Limin; Zhong, Duo] Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.;[Shen, Xianjun; Yu, Limin; Zhong, Duo] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan, Peoples R China.
通讯机构：
[Shen, Xianjun] C;Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.;Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan, Peoples R China.
关键词：
Laplace normalization;LncRNA;miRNA-disease association prediction;three-layer heterogeneous network;unbalanced random walk
作者机构：
[Jiang, Xingpeng; Yang, Jincai; He, Tingting; Shen, Xianjun; Hu, Xiaohua; Shen, XJ; Gong, Xue] Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.;[Hu, Xiaohua] Drexel Univ, Coll Comp & Informat, Philadelphia, PA 19104 USA.
会议名称：
IEEE International Conference on Bioinformatics and Biomedicine (BIBM) - Human Genomics
会议时间：
DEC 03-06, 2018
会议地点：
Madrid, SPAIN
会议主办单位：
[Shen, Xianjun;Gong, Xue;Jiang, Xingpeng;Yang, Jincai;He, Tingting;Hu, Xiaohua] Cent China Normal Univ, Sch Comp, Wuhan, Hubei, 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：
weighted Directed motifs;microbial network;high order structures;motif-based clustering
摘要：
High-order connectivity patterns are essential to understanding the basic structure of complex networks. Network motifs are considered as the basic building blocks of complex networks. From identifying network motifs to discovering higher-order modular organizations by them, it is helpful to study the organization principles and functional modules of the biological networks in a divide-and-conquer manner. However, the current research based on network motifs often neglect the influence of weight in network motifs. In this paper, the concept of weighted motifs was presented and was applied to microbial network. The method was proposed to find the optimal weighted motif in microbial network and analyze the high-order structure of weighted networks based on them. It also proved that the partially weighted motifs can obtain optimal clusters in theory over unweighted ones.
期刊：
2017 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM),2017年2017-January:1338-1344 ISSN：2156-1125
通讯作者：
Yang, Jincai
作者机构：
[Jiang, Xingpeng; Yang, Jincai; Shen, Xianjun; Hu, Xiaohua; Guo, Chunjie] Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Yang, Jincai] C;Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Hubei, Peoples R China.
会议名称：
2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
会议时间：
November 2017
会议地点：
Kansas City, MO, USA
会议主办单位：
[Yang, Jincai;Guo, Chunjie;Jiang, Xingpeng;Hu, Xiaohua;Shen, Xianjun] Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Hubei, Peoples R China.
会议论文集名称：
2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
关键词：
microRNA;tumor-associated genes in mouse;network;conservative analysis;GO analysis;support vector machine
摘要：
Gene (microRNA) identification is a key step in understanding the cellular mechanisms. Compared with biological experiments, computational prediction of disease genes is cheaper and more effortless. In this study, we analyzed the properties of tumor-associated microRNA in mouse and found that tumor-associated genes display 8distinguishingfeatures when compared with genes not yet known to be involved in tumor. The features of tumor-associated genes tend to located at network center and interact with each other were found by analyze the network characteristics. In addition, the features of the tumor-associated genes tend to be involved in certain biological processes and show certain phenotypes also were found through enrichment analysis. Based on these features, a machine-learning algorithm SVM were developed to predict new tumor-associated genes in mouse. Using the machine-learning algorithm, 120 tumor-associated genes were predicted with a posterior probability more than 0.9. We verified the accuracy of the identification framework with the data set of tumor-associated genes, and the result shows that this method is feasible.
摘要：
The microbiota living in the human body plays a very important role in our health and disease, so the identification of microbes associated with diseases will contribute to improving medical care and to better understanding of microbe functions, interactions. However, the known associations between the diseases and microbes are very less. We proposed a new method for prioritization of candidate microbes to predict disease-microbe relationships that based on the random walking on the heterogeneous network. Here, we first constructed a heterogeneous network by connecting the disease network and microbe network using the disease-microbe relationship information, then extended the random walk to the heterogeneous network, finally we used leave-one-out cross-validation to evaluate the method and ranked the candidate disease-causing microbes. We used the algorithm to disclose some potential association between disease and microbe that cannot be found by microbe network or disease network alone. Furthermore, we studied three representative diseases, Type 2 diabetes, Asthma and Psoriasis, and presented the potential microbes associated with these diseases, respectively. We confirmed that the discovery of the associations will be a good clinical solution for disease mechanism understanding, diagnosis and therapy.
期刊：
2016 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM),2017年:1283-1287 ISSN：2156-1125
通讯作者：
Shen, Xianjun
作者机构：
[Jiang, Xingpeng; Yang, Jincai; Shen, Xianjun; Hu, Xiaohua; Huang, Qingyang; Gu, Huichao] Cent China Normal Univ, Sch Comp Sci, Wuhan, Peoples R China.
通讯机构：
[Shen, Xianjun] C;Cent China Normal Univ, Sch Comp Sci, Wuhan, Peoples R China.
会议名称：
2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
会议时间：
December 2016
会议地点：
Shenzhen, China
会议主办单位：
[Yang, Jincai;Gu, Huichao;Jiang, Xingpeng;Huang, Qingyang;Hu, Xiaohua;Shen, Xianjun] Cent China Normal Univ, Sch Comp Sci, Wuhan, Peoples R China.
会议论文集名称：
2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
关键词：
osteoporosis;GWAS;SNPs;associated genes;random walk;PPI network;ID3 decision tree algorithm
摘要：
While much progress has been made on the genetic analysis of osteoporosis in the past 20 years, there are a lot of genes and SNPs that are associated with osteoporosis through GWAS. In this paper, we aim to identify the risky SNPs associated with osteoporosis by algorithms based on the known osteoporosis GWAS-associated SNPs. The whole framework of our prediction method includes two steps: Firstly, we identify whether the associated genes of the suspected risky SNPs is osteoporosis GWAS-associated genes by the method of random walk algorithm on the PPI network of osteoporosis GWAS-associated genes. Then, we classify the positive result SNPs based on their features of position and function through ID3 decision tree algorithm. We verify the accuracy of the prediction framework with the data set of GWAS-associated SNPs, and the result shows that the method is feasible. It provides a more convenient way to identify the risky SNPs of osteoporosis associated.
作者机构：
[Jiang, Xingpeng; Xie, Wei; Yang, Jincai; He, Tingting; Shen, Xianjun; Hu, Po; Hu, Xiaohua; Yi, Li] Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.;[Yi, Li] Letv Cloud Comp Co Ltd, Beijing, Peoples R China.;[Hu, Xiaohua] Drexel Univ, Coll Comp & Informat, Philadelphia, PA 19104 USA.
通讯机构：
[Shen, Xianjun] C;Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.
关键词：
Protein complexes;Algorithms;Protein interaction networks;Gene expression;Protein interactions;Forecasting;Genetic networks;Yeast
摘要：
How to identify protein complex is an important and challenging task in proteomics. It would make great contribution to our knowledge of molecular mechanism in cell life activities. However, the inherent organization and dynamic characteristic of cell system have rarely been incorporated into the existing algorithms for detecting protein complexes because of the limitation of protein-protein interaction (PPI) data produced by high throughput techniques. The availability of time course gene expression profile enables us to uncover the dynamics of molecular networks and improve the detection of protein complexes. In order to achieve this goal, this paper proposes a novel algorithm DCA (Dynamic Core-Attachment). It detects protein-complex core comprising of continually expressed and highly connected proteins in dynamic PPI network, and then the protein complex is formed by including the attachments with high adhesion into the core. The integration of core-attachment feature into the dynamic PPI network is responsible for the superiority of our algorithm. DCA has been applied on two different yeast dynamic PPI networks and the experimental results show that it performs significantly better than the state-of-the-art techniques in terms of prediction accuracy, hF-measure and statistical significance in biology. In addition, the identified complexes with strong biological significance provide potential candidate complexes for biologists to validate.
摘要：
As we all know, the microbiota show remarkable variability within individuals. At the same time, those microorganisms living in the human body play a very important role in our health and disease, so the identification of the relationships between microbes and diseases will contribute to better understanding of microbes interactions, mechanism of functions. However, the microbial data which are obtained through the related technical sequencing is too much, but the known associations between the diseases and microbes are very less. In bioinformatics, many researchers choose the network topology analysis to solve these problems. Inspired by this idea, we proposed a new method for prioritization of candidate microbes to predict potential disease-microbe association. First of all, we connected the disease network and microbe network based on the known disease-microbe relationships information to construct a heterogeneous network, then we extended the random walk to the heterogeneous network, and used leave-one-out cross-validation and ROC curve to evaluate the method. In conclusion, the algorithm could be effective to disclose some potential associations between diseases and microbes that cannot be found by microbe network or disease network only. Furthermore, we studied three representative diseases, Type 2 diabetes, Asthma and Psoriasis, and finally presented the potential microbes associated with these diseases by ranking candidate disease-causing microbes, respectively. We confirmed that the discovery of the new associations will be a good clinical solution for disease mechanism understanding, diagnosis and therapy.
期刊：
2016 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM),2017年:1269-1274 ISSN：2156-1125
通讯作者：
Xie, Dan
作者机构：
[Jiang, Xingpeng; Yang, Jincai; He, Tingting; Shen, Xianjun; Hu, Xiaohua; Zhou, Jin] Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.;[Xie, Dan] Hubei Univ Chinese Med, Coll Informat Engn, Wuhan, Peoples R China.
通讯机构：
[Xie, Dan] H;Hubei Univ Chinese Med, Coll Informat Engn, Wuhan, Peoples R China.
会议名称：
2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
会议时间：
December 2016
会议地点：
Shenzhen
会议主办单位：
[Shen, Xianjun;Zhou, Jin;Jiang, Xingpeng;Hu, Xiaohua;He, Tingting;Yang, Jincai] Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.^[Xie, Dan] Hubei Univ Chinese Med, Coll Informat Engn, Wuhan, Peoples R China.
会议论文集名称：
2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
关键词：
protein complex;dynamic protein-protein interaction network;cluster center;gene ontology information;brainstorming process
摘要：
Detection of protein complexes and functional modules plays a crucial role for strengthening the comprehension of cellular organization and biological functions on the dynamic protein-protein interaction network. In this article, we put forward a new strategy to identify temporal protein complexes. Integrating time-course gene expression data into static protein interaction data, a series of time-sequenced subnetworks were constructed. Then we combined the network topology and gene ontology information for defining the distance between proteins in PPI network. A novel method to find the cluster centers and then form initial clusters was based on the idea that cluster centers are usually recognized as nodes with higher densities than their neighbors and with a relatively larger distance from other cluster centers. Finally, inspired by the brainstorming discussion process, two ways are introduced to update the initial clusters for achieving the optimal results. After the filtering and merging procedure, experimental results demonstrated that the proposed strategy had a good performance comparing with the other four advanced algorithms - MCODE, FAG-EC, HC-PIN, and CNC.
摘要：
Genome-wide association studies (GWAS) of T2D have discovered a number of loci that contribute to susceptibility to the disease. In this paper, we classified and identified the suspected risky Loci of T2D with computational method based on the known T2D GWAS-associated SNPs. The framework includes two parts: we first classified the SNPs based on their features of position and function through a simplified classification decision tree which was constructed by C4.5 decision tree algorithm; we then identified whether the genes associated with the suspected risky SNPs are associated with T2D by using random walk algorithm with Restart Model on the PPI network of T2D GWAS-associated genes among proteins and interactors. Based on the classification of SNP associated with T2D, we analyzed molecular pathogenesis of T2D. We verified the accuracy and reliability of the classification and identification framework with the data set of GWAS-associated SNPs. The result shows that this method is reliable. It provides a significant way to identify and classify the suspected risky Loci associated with T2D and further insights into the molecular pathogenesis of T2D.
