会议名称：
IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
会议时间：
NOV 18-21, 2019
会议地点：
San Diego, CA
会议主办单位：
[Li, Xusheng;Fu, Chengcheng;Zhong, Ran;Zhong, Duo;He, Tingling;Jiang, Xingpeng] Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.^[Li, Xusheng;Fu, Chengcheng;Zhong, Ran;Zhong, Duo;He, Tingling;Jiang, Xingpeng] Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan, Hubei, Peoples R China.
会议论文集名称：
IEEE International Conference on Bioinformatics and Biomedicine-BIBM
关键词：
Text Mining;Bacterial Named Entity Recognition;Language Model;Microbial Interaction
摘要：
Interactions among microorganisms have been the key to understand microbial communities. As an important member of microorganisms, bacteria are closely related to human diseases. Therefore, studying the interaction between bacteria plays an important role in microbiome research. There are a large number of published medical literatures that contain small-scale data about the interactions between bacteria. These literatures often record the bacteria interactions discovered by co -cultural experiments for two or more species. Mining and organizing them into databases will provide reliable support for microbiome research. Named entity recognition (NER) is an essential step of interaction extraction (IE) by automatically identifying bacterial entities in the text. In this paper, we propose a method based on language model for identifying bacteria named entities. Using the language model to learn the semantic information between words, the F1 score reaches 96.14%, which is the best performance in bacteria NER compared with the previous experimental results.
作者机构：
[Jiang, Xingpeng; Yang, Jincai; Ma, Shunping] Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.
会议名称：
IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
会议时间：
NOV 18-21, 2019
会议地点：
San Diego, CA
会议主办单位：
[Yang, Jincai;Ma, Shunping;Jiang, Xingpeng] Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.
会议论文集名称：
IEEE International Conference on Bioinformatics and Biomedicine-BIBM
关键词：
lncRNA;disease;autoencoder;rotation forest
摘要：
In the past few years, most disease-related lncRNAs have been identified, but the experimental identification is cost-consuming and time-consuming. It is therefore very important to develop a reliable computational model to predict lncRNA-disease association. In this paper, we propose a method based on similarity, combining autoencoder and rotation forest to predict lncRNA-disease association (SARLDA). This method not only makes use of disease and lncRNA similarities, but also extracts latent low-dimension features and expand the gap between samples to make it easier to predict the associations. To evaluate our method, we conducted several experiments. Sufficient validations show that this method has significantly improved the prediction performance.
作者机构：
[Zhang, Jinyong; Zhao, Weizhong; Fang, Dandan; He, Tingting; Jiang, Xingpeng] Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.;[Zhao, Weizhong] Cent China Normal Univ, Hubei Key Lab Artificial Intelligence & Smart Lea, Wuhan, Peoples R China.;[Zhao, Weizhong] Guilin Univ Elect Technol, Guangxi Key Lab Trusted Software, Guilin, Peoples R China.;[Xu, Xiaowei] Univ Arkansas, Dept Informat Sci, Little Rock, AR 72204 USA.;[Hu, Xiaohua] Drexel Univ, Coll Comp & Informat, Philadelphia, PA 19104 USA.
会议名称：
IEEE International Conference on Big Data (Big Data)
会议时间：
DEC 09-12, 2019
会议地点：
Los Angeles, CA
会议主办单位：
[Fang, Dandan;Zhang, Jinyong;Zhao, Weizhong;Jiang, Xingpeng;He, Tingting] Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.^[Zhao, Weizhong] Cent China Normal Univ, Hubei Key Lab Artificial Intelligence & Smart Lea, Wuhan, Peoples R China.^[Zhao, Weizhong] Guilin Univ Elect Technol, Guangxi Key Lab Trusted Software, Guilin, Peoples R China.^[Xu, Xiaowei] Univ Arkansas, Dept Informat Sci, Little Rock, AR 72204 USA.^[Hu, Xiaohua] Drexel Univ, Coll Comp & Informat, Philadelphia, PA 19104 USA.
会议论文集名称：
IEEE International Conference on Big Data
关键词：
semantic hierarchy;document structure;attention model;biomedical document classification;adaptive class cost learning
摘要：
Biomedical document classification is a fundamental task in biomedical field. Existing methods do not make full use of the hierarchically semantic structures in biomedical documents which can be utilized to improve the performance of biomedical document classification. In this paper, according to the hierarchical structures in given biomedical documents, we propose two models for biomedical document classification, which are based on the semantically hierarchical attention mechanism. Specifically, we utilize a hierarchical attention mechanism to model biomedical documents, taking into account simultaneously multiple-level semantic relationships in documents. In addition, an adaptive cost sensitive learning method is proposed to address the data imbalance issue. Extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed methods.
作者机构：
[Han, Chang; Zhu, Qiang; He, Tingting; Zhu, Qing; Jiang, Xingpeng] Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.;[Zhu, Qiang; He, Tingting; Zhu, Qing; Jiang, Xingpeng] Cent China Normal Univ, Hubei Key Lab Artificial Intelligence & Smart Lea, Wuhan, Peoples R China.
会议名称：
IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
会议时间：
NOV 18-21, 2019
会议地点：
San Diego, CA
会议主办单位：
[Zhu, Qing;Han, Chang;Zhu, Qiang;He, Tingting;Jiang, Xingpeng] Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.^[Zhu, Qing;Zhu, Qiang;He, Tingting;Jiang, Xingpeng] Cent China Normal Univ, Hubei Key Lab Artificial Intelligence & Smart Lea, Wuhan, Peoples R China.
会议论文集名称：
IEEE International Conference on Bioinformatics and Biomedicine-BIBM
关键词：
biomarker;metabolite-disease association prediction;matrix factorization;convolutional neural network;gated recurrent unit network
摘要：
Metabolic disorders play an important role in the development of many common diseases, including obesity, diabetes and coronary heart disease. Identifying key metabolites associated with disease can help us understand the mechanism of disease better and improve clinical diagnosis. Predicting diseases-related metabolites through computational approaches can provide potential biomarkers for further biological experiments. Text annotations on metabolites in existing databases provide prior information, which could provide more information about metabolites. However, current approaches haven't taken this information into consideration. In this work, we proposed a probability matrix factorization method which combined deep textual features to predict metabolite-disease associations. The deep neural network combining convolutional neural network and gated recurrent unit network is used to extract the corresponding features from text annotations of metabolites and diseases. Then, associations between metabolites and diseases are predicted through the matrix factorization based on these textual features. The main contributions in the work is that our model shows that adding textual features could help to improve the prediction of metabolite-disease associations. Case studies have indicated our model got predictive ability for diseases-related metabolites.
摘要：
Biomedical event extraction has wide applications in biomedicine field. As a prerequisite step in biomedical event extraction, event trigger identification has attracted growing attention in biomedical research. Although many approaches have been proposed for biomedical event trigger identification, two main challenges still remain for researchers: 1) most of the existing approaches treat each sentence separately in biomedical documents, failing to make full use of the semantics in the global document context; 2) the sparseness of event triggers leads to a serious issue of imbalanced class for trigger identification. In this paper, we propose an end-to-end framework for biomedical event trigger identification which addresses effectively the two mentioned challenges accordingly. Specifically, a hierarchical attention mechanism is used to model the global document context, including the semantic relationships both among words in the same sentence and among sentences in the same document. In addition, an adaptive class weight learning method is proposed to treat the class imbalance issue in biomedical event trigger identification. Experimental results on two commonly used datasets demonstrate the effectiveness of the proposed framework.
摘要：
Microorganisms play a vital role in various ecosystems, but their complex interaction is still unclear. With the publication of a large number of microbial literatures, many experimentally verified microbial interaction is dispersed therein. Organizing them into a database or knowledge graph can facilitate the development of microbiology research. Text mining technology is able to automatically extract and integrate these microbial interactions, as well as discover implicit information in literatures. For this purpose, we manually annotate a Microbial Interaction Corpus (MICorpus) containing 1005 abstracts, which provide a useful data source for the MIE task. On this basis, we propose an automated MIE extraction system based on Max-Bi-LSTM model. The best result of the system is precision (P) 76.313%, recall (R) of 90.121%, and an F value (F) 82.476%.
作者机构：
[Ma, Yingjun] Cent China Normal Univ, Sch Math & Stat, Wuhan, Hubei, Peoples R China.;[Hu, Xiaohua; Yu, Limin; He, Tingting; Jiang, Xingpeng] Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.
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：
[Ma, Yingjun] Cent China Normal Univ, Sch Math & Stat, Wuhan, Hubei, Peoples R China.^[Yu, Limin;He, Tingting;Hu, Xiaohua;Jiang, Xingpeng] Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.
会议论文集名称：
IEEE International Conference on Bioinformatics and Biomedicine-BIBM
摘要：
Long non-coding RNA (1ncRNA) has a close relationship with multiple biological processes and complex diseases. Generally speaking, it functions through the interaction with corresponding RNA-binding proteins. However, it is costly and time-consuming to use experimental methods to detect IncRNA-protein interactions. Network-based prediction methods have been developed recently, but very few methods consider the integration of multiple features and the non-linear relationship of IncRNAs (proteins). In this paper, we propose a kernel-based soft-neighborhood propagation algorithm (LKSNS) to predict the potential 1ncRNA-protein interactions. The method not only makes use of the non-neighborhood information, but also excavates the potential non-linear relationship. We compare LKSNS with other state-of-the-art methods based on multiple datasets and the results show that LKSNS has significantly better prediction performance. The case study further demonstrates that the LKSNS has the good practicality for 1ncRNA-protein interaction prediction.
