作者机构：
[Jiang, Xingpeng; He, Tingting; Zhao, Weizhong; Zhao, Yao] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan 430079, Hubei, Peoples R China.;[Jiang, Xingpeng; He, Tingting; Zhao, Weizhong; Zhao, Yao] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.;[Jiang, Xingpeng; He, Tingting; Zhao, Weizhong; Zhao, Yao] 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：
[Weizhong Zhao] H;Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University , Wuhan, Hubei 430079, China<&wdkj&>School of Computer, Central China Normal University, Wuhan, Hubei 430079, China<&wdkj&>National Language Resources Monitoring & Research Center for Network Media, Central China Normal University , Wuhan, Hubei 430079, China<&wdkj&>Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology , Guilin 541004, China<&wdkj&>Guangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University , Guilin 541004, China
作者机构：
[Xie, Bo; Liu, Fei; Ge, Leixin; Zhou, Dan; Shi, Zunji; Zhao, Na; Wu, Gang; Zheng, Ningning] Cent China Normal Univ, Sch Life Sci, Hubei Key Lab Genet Regulat & Integrat Biol, Wuhan 430079, Peoples R China.;[Zhou, Dan] Guizhou Normal Coll, Sch Biol Sci, Guiyang 550018, Guizhou, Peoples R China.;[Jiang, Xingpeng] Cent China Normal Univ, Sch Comp, Wuhan 430079, Peoples R China.;[Halverson, Larry] Iowa State Univ, Dept Plant Pathol & Microbiol, Ames, IA USA.
通讯机构：
[Xie, Bo] C;Cent China Normal Univ, Sch Life Sci, Hubei Key Lab Genet Regulat & Integrat Biol, Wuhan 430079, Peoples R China.
摘要：
Microbial taxon-taxon co-occurrences may directly or indirectly reflect the potential relationships between the members within a microbial community. However, to what extent and the specificity by which these co-occurrences are influenced by environmental factors remains unclear. In this report, we evaluated how the dynamics of microbial taxon-taxon co-occurrence is associated with the changes of environmental factors in Nan Lake at Wuhan city, China with a Modified Liquid Association method. We were able to detect more than one thousand taxon-taxon co-occurrences highly correlated with one or more environmental factors across a phytoplankton bloom using 16S rRNA gene amplicon community profiles. These co-occurrences, referred to as environment dependent co-occurrences (ED_co-occurrences), delineate a unique network in which a taxon-taxon pair exhibits specific, and potentially dynamic correlations with an environmental parameter, while the individual relative abundance of each may not. Microcystis involved ED_co-occurrences are in important topological positions in the network, suggesting relationships between the bloom dominant species and other taxa could play a role in the interplay of microbial community and environment across various bloom stages. Our results may broaden our understanding of the response of a microbial community to the environment, particularly at the level of microbe-microbe associations. This article is protected by copyright. All rights reserved.
期刊：
Frontiers in Genetics,2020年11:546210 ISSN：1664-8021
通讯作者：
Jiang, Xingpeng
作者机构：
[Zhu, Qiang] Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.;[Jiang, Xingpeng; He, Tingting; Pan, Min; Zhu, Qiang; Zhu, Qing] Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.;[Jiang, Xingpeng; He, Tingting; Pan, Min; Zhu, Qing] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan, Peoples R China.
通讯机构：
[Jiang, Xingpeng] C;Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.;Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan, Peoples R China.
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：
Lecture Notes in Computer Science,2020年12435:79-90 ISSN：0302-9743
作者机构：
Hubei Provincial Key Laboratory of Artificial Intelligence and Smart LearningCentral China Normal UniversityWuhanPR China;School of ComputerCentral China Normal UniversityWuhanPR China
摘要：
Following the rapid advances of the human microbiome, the importance of micro-organisms especially bacteria is gradually recognized. The interactions among bacteria and their host are particulary important for understanding the mechanism of microbe-relate diseases. This article mainly introduces an explorative study to extract the relations between bacteria and diseases based on biomedical text mining. We have constructed a Microbe-Disease Knowledge Graph (MDKG) through integrating multi-source heterogeneous data from Wikipedia text and other related databases. Specifically, we introduce the word embedding obtained from biomedical literature into traditional method. Results show that the pre-trained relation vectors can better represent the real associations between entities. Therefore, the construction of MDKG can also provide a new way to predict and analyse the associations between microbes and diseases based on text mining.
作者机构：
[Sun, Bo; Pan, Min] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.;[Pan, Min] Hubei Normal Univ, Sch Comp & Informat Engn, Huangshi 435002, Hubei, Peoples R China.;[Jiang, Xingpeng; He, Tingting; Zhang, Yue; Zhu, Qiang] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[He, Tingting] C;Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.
会议名称：
International Conference on Intelligent Computing (ICIC) / Intelligent Computing and Biomedical Informatics (ICBI) Conference - Medical Informatics and Decision Making
会议时间：
AUG 15-18, 2018
会议地点：
PEOPLES R CHINA
会议主办单位：
[Pan, Min;Sun, Bo] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.^[Pan, Min] Hubei Normal Univ, Sch Comp & Informat Engn, Huangshi 435002, Hubei, Peoples R China.^[Zhang, Yue;Zhu, Qiang;He, Tingting;Jiang, Xingpeng] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.
作者机构：
[Ma, Yingjun] Cent China Normal Univ, Sch Math & Stat, Wuhan, Hubei, Peoples R China.;[Jiang, Xingpeng; He, Tingting; Ma, Yingjun] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan, Hubei, Peoples R China.;[Jiang, Xingpeng; He, Tingting] Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.
通讯机构：
[Jiang, Xingpeng] C;Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan, Hubei, Peoples R China.;Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.
摘要：
Many long ncRNAs (lncRNA) make their effort by interacting with the corresponding RNA-binding proteins, and identifying the interactions between lncRNAs and proteins is important to understand the functions of lncRNA. Compared with the time-consuming and laborious experimental methods, more and more computational models are proposed to predict lncRNA-protein interactions. However, few models can effectively utilize the biological network topology of lncRNA (protein) and combine its sequence structure features, and most models cannot effectively predict new proteins (lncRNA) that do not interact with any lncRNA (proteins). In this study, we proposed a projection-based neighborhood non-negative matrix decomposition model (PMKDN) to predict potential lncRNA-protein interactions by integrating multiple biological features of lncRNAs (proteins). First, according to lncRNA (protein) sequences and lncRNA expression profile data, we extracted multiple features of lncRNA (protein). Second, based on protein GO ontology annotation, lncRNA sequences, lncRNA(protein) feature information, and modified lncRNA-protein interaction network, we calculated multiple similarities of lncRNA (protein), and fused them to obtain a more accurate lncRNA(protein) similarity network. Finally, combining the similarity and various feature information of lncRNA (protein), as well as the modified interaction network, we proposed a projection-based neighborhood non-negative matrix decomposition algorithm to predict the potential lncRNA-protein interactions. On two benchmark datasets, PMKDN showed better performance than other state-of-the-art methods for the prediction of new lncRNA-protein interactions, new lncRNAs, and new proteins. Case study further indicates that PMKDN can be used as an effective tool for lncRNA-protein interaction prediction.
期刊：
Frontiers in Genetics,2019年10:491009 ISSN：1664-8021
通讯作者：
Jiang, Xingpeng
作者机构：
[Zhu, Qiang] Cent China Normal Univ, Sch Informat Management, Wuhan, Hubei, Peoples R China.;[Jiang, Xingpeng; He, Tingting; Pan, Min; Zhu, Qiang; Zhu, Qing] Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.;[Jiang, Xingpeng; He, Tingting; Pan, Min; Zhu, Qing] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan, Hubei, Peoples R China.
通讯机构：
[Jiang, Xingpeng] C;Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.;Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan, Hubei, Peoples R China.
摘要：
The microbiome-wide association studies are to figure out the relationship between microorganisms and humans, with the goal of discovering relevant biomarkers to guide disease diagnosis. However, the microbiome data is complex, with high noise and dimensions. Traditional machine learning methods are limited by the models' representation ability and cannot learn complex patterns from the data. Recently, deep learning has been widely applied to fields ranging from text processing to image recognition due to its efficient flexibility and high capacity. But the deep learning models must be trained with enough data in order to achieve good performance, which is impractical in reality. In addition, deep learning is considered as black box and hard to interpret. These factors make deep learning not widely used in microbiome-wide association studies. In this work, we construct a sparse microbial interaction network and embed this graph into deep model to alleviate the risk of overfitting and improve the performance. Further, we explore a Graph Embedding Deep Feedforward Network (GEDFN) to conduct feature selection and guide meaningful microbial markers' identification. Based on the experimental results, we verify the feasibility of combining the microbial graph model with the deep learning model, and demonstrate the feasibility of applying deep learning and feature selection on microbial data. Our main contributions are: firstly, we utilize different methods to construct a variety of microbial interaction networks and combine the network via graph embedding deep learning. Secondly, we introduce a feature selection method based on graph embedding and validate the biological meaning of microbial markers. The code is available at https://github.com/MicroAVA/GEDFN.git.
作者机构：
[Jiang, Xingpeng; He, Tingting; Fu, Chengcheng; Li, Xusheng; Zhong, Duo] Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.;[Jiang, Xingpeng; He, Tingting; Fu, Chengcheng; Li, Xusheng; Zhong, Duo] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan, Hubei, Peoples R China.;[Zhong, Ran] Cent China Normal Univ, Collaborat & Innovat Ctr, Wuhan, Hubei, Peoples R China.
通讯机构：
[Jiang, Xingpeng] C;Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.;Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan, Hubei, Peoples R China.
会议名称：
IEEE International Conference on Bioinformatics and Biomedicine (BIBM) - Bioinformatics and Systems Biology
会议时间：
DEC 03-06, 2018
会议地点：
Madrid, SPAIN
会议主办单位：
[Li, Xusheng;Fu, Chengcheng;Zhong, Duo;He, Tingting;Jiang, Xingpeng] Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.^[Li, Xusheng;Fu, Chengcheng;Zhong, Duo;He, Tingting;Jiang, Xingpeng] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan, Hubei, Peoples R China.^[Zhong, Ran] Cent China Normal Univ, Collaborat & Innovat Ctr, Wuhan, Hubei, Peoples R China.
关键词：
Named entity recognition;Biomedical text mining;Conditional random field;Deep learning
作者机构：
[Ma, Yingjun] Cent China Normal Univ, Sch Math & Stat, Wuhan 430079, Hubei, Peoples R China.;[Jiang, Xingpeng; He, Tingting; Zhang, Chenhao] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.;[Jiang, Xingpeng; He, Tingting] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan 430079, Hubei, Peoples R China.;[Ge, Leixin] Cent China Normal Univ, Sch Life Sci, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Jiang, Xingpeng] C;Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.;Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan 430079, Hubei, Peoples R China.
会议名称：
IEEE International Conference on Bioinformatics and Biomedicine (BIBM) - Medical Genomics
会议时间：
DEC 03-06, 2018
会议地点：
Madrid, SPAIN
会议主办单位：
[Ma, Yingjun] Cent China Normal Univ, Sch Math & Stat, Wuhan 430079, Hubei, Peoples R China.^[He, Tingting;Zhang, Chenhao;Jiang, Xingpeng] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.^[He, Tingting;Jiang, Xingpeng] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan 430079, Hubei, Peoples R China.^[Ge, Leixin] Cent China Normal Univ, Sch Life Sci, Wuhan 430079, Hubei, Peoples R China.
作者机构：
[Jiang, Xingpeng; He, Tingting; Hu, Xiaohua; Li, Xusheng; Wang, Xiaoyan; Zhong, Duo] Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.;[Hu, Xiaohua] Drexel Univ, Sch Comp & Informat, Philadelphia, PA 19104 USA.;[Zhong, Ran] Cent China Normal Univ, Collaborat & Innovat Ctr, Wuhan, Hubei, Peoples R China.
会议名称：
IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
会议时间：
DEC 03-06, 2018
会议地点：
Madrid, SPAIN
会议主办单位：
[Li, Xusheng;Wang, Xiaoyan;Zhong, Duo;He, Tingting;Hu, Xiaohua;Jiang, Xingpeng] Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.^[Hu, Xiaohua] Drexel Univ, Sch Comp & Informat, Philadelphia, PA 19104 USA.^[Zhong, Ran] Cent China Normal Univ, Collaborat & Innovat Ctr, Wuhan, Hubei, Peoples R China.
会议论文集名称：
IEEE International Conference on Bioinformatics and Biomedicine-BIBM
关键词：
biomedical text mining;bacterial named entity recognition;conditional random field;deep learning;microbial interaction
摘要：
Microorganisms have been confirmed to be essential for the fundamental function of various ecosystems. The interactions among microorganisms affect the human health and environmental ecosystem. A large number of microbial interactions with experimental confidence have been reported in biomedical literature. Extracting and collating these interactions with experimental confidence into a database will create a valuable data resource. Named Entity Recognition (NER) is the premise and key to interaction extraction from literatures. Especially, bacterial named entity recognition is still a challenging task due to the specialty of bacterial names. In this paper, we propose a bacterial named entity recognition system based on a hybrid deep learning framework (HDL-CRF), which integrates two deep learning models: the bidirectional long short-term memory network and the convolutional neural network, as well as the conditional random field approach, for automatically extracting the features. Finally, we prove that this model outperforms previous methods in performance.
摘要：
Virus-host association studies are significant for understanding the complex functions and dynamics of microbial communities of human health or diseases. Several virus-host association prediction methods have been developed based on the information of sequences, virus networks, host networks and virus-host networks separately. In this study, we develop a heterogeneous network approach based on neighborhood regularization logistic matrix factorization (LMFH-VH) which integrate the virus similarity network and the host similarity network using known virus-host associations. The virus similarity network and the host similarity network were constructed based on oligonucleotide frequency measures and Gaussian interaction profile kernel similarity, respectively. LMFH-VH achieves the best performance on several validation datasets comparing with other four network-based methods. The host prediction accuracy of LMFH-VH is 24.17% and 12.8% higher than two recently proposed virus-host prediction methods, respectively. The codes and datasets are available at https://github.com/liudan111/LMFH-VH.git.
期刊：
Lecture Notes in Computer Science,2018年10955:93-99 ISSN：0302-9743
通讯作者：
Zhang, Yue
作者机构：
[Pan, Min] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.;[Jiang, Xingpeng; He, Tingting; Zhang, Yue] Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Zhang, Yue] C;Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Hubei, Peoples R China.
会议名称：
14th International Conference on Intelligent Computing (ICIC)
会议时间：
AUG 15-18, 2018
会议地点：
Wuhan, PEOPLES R CHINA
会议主办单位：
[Pan, Min] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.^[Zhang, Yue;He, Tingting;Jiang, Xingpeng] Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Hubei, Peoples R China.
摘要：
In an actual electronic health record (EHR), patient notes are written with terse language and clinical jargons. However, most Pseudo Relevance Feedback (PRF) technique methods do not take into account the significant degree of candidate term in feedback documents and the co-occurrence relationship between a candidate term and a query term simultaneously. In this paper, we study how to incorporate proximity information into the Rocchio's model, and propose a HAL-based Rocchio's model, called HRoc. A new concept of term proximity feedback weight is introduced to model in the query expansion. Then, we propose three normalization methods to incorporate proximity information. Experimental results on 2016 TREC Clinical Support Medicine collections show that our proposed models are effective and generally superior to the state-of-the-art relevance feedback models.
