2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)最新文献_第5页

Bipartite graphs for metagenomic data analysis and visualization 用于宏基因组数据分析和可视化的二部图

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359839

K. Sedlář, Helena Skutková, P. Videnska, I. Rychlík, I. Provazník

{"title":"Bipartite graphs for metagenomic data analysis and visualization","authors":"K. Sedlář, Helena Skutková, P. Videnska, I. Rychlík, I. Provazník","doi":"10.1109/BIBM.2015.7359839","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359839","url":null,"abstract":"Metagenomics became very popular after expansion of next-generation sequencing techniques that allowed simple implementation of extensive studies. With a target gene sequencing approach, an identification of organisms in a metagenome is quite effortless since only a small reference database of the particular gene is needed. Moreover, by counting the copies of individual genes, also quantitative analysis can be applied. Unfortunately, current bioinformatics tools aim mainly on the analysis of a single metagenome. A cluster analysis, a heatmap of correlation coefficients, biclustering or other statistics techniques can only show relations inside the metagenome or the relation between the metagenome composition and other parameters. On the other hand, there is a lack of tools to provide a comparative analysis of two or more metagenomes. Suitable properties for this kind of analysis can be found in a bipartite graph. Here, we present a novel workflow for finding the suitable representation of metagenomic data to provide a comparative analysis of metagenomes. The resulting graph can take into account information about the actual composition of the metagenome as well as the environment it relates to. Thus, it can provide different view of the data to the naked eye that can complement other techniques such as principal coordinate analysis.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131136991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Directed cyclic graph-based feature selection and modeling of the dampness syndrome of chronic gastritis 基于有向循环图的慢性胃炎湿证特征选择与建模

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359820

Wei-Fei Xu, Guoping Liu, Jian-jun Yan, Yiqin Wang, Xiong Lu, Tao Zhong

{"title":"Directed cyclic graph-based feature selection and modeling of the dampness syndrome of chronic gastritis","authors":"Wei-Fei Xu, Guoping Liu, Jian-jun Yan, Yiqin Wang, Xiong Lu, Tao Zhong","doi":"10.1109/BIBM.2015.7359820","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359820","url":null,"abstract":"This study aimed to investigate the feasibility of the directed cyclic graph (DCG) in the feature selection and modeling of dampness syndrome to objectively diagnose chronic gastritis (CG). The diagnostic information of patients with dampness syndrome was selected from 919 cases collected in our previous study. Relevant characteristic variables were chosen using the combined rough set and mutual information (RS-MI) method. These selected variables were then used to construct a DCG model. The selected variables were consistent with the symptoms described in traditional Chinese medicine (TCM). The classification accuracies of both dampness syndromes were determined through DCG modeling. The accuracies of the dampness-heat accumulating in the spleen-stomach and the dampness obstructing the spleen-stomach were 90.4% and 78.7%, respectively. Therefore, the DCG model was superior to Navie Bayes(NB) model in terms of classification ability. The classification accuracy rate of the DCG model of the dampness obstructing the spleen-stomach was higher by 1.1% than that of the NB model. In conclusion,feature selection and model construction methods can be used to objectively evaluate the TCM syndromes of CG; nevertheless, these methods should be further investigated and promoted.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127834336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Electronic Health Record: A review 电子健康记录:综述

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359879

M. Graña, K. Jackowski

引用次数: 12

A distributional approach to summarization of radiology reports 一种分布的方法来总结放射学报告

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359815

Eamon Johnson, W. Baughman, G. Özsoyoglu

引用次数: 1

Influence of heart rate variability in healthy subjects with respiratory manipulation 呼吸操作对健康受试者心率变异性的影响

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359932

Wen-Chen Lin, T. Fu, Sheng-Cheng Huang, Cheng-Lun Tsai, Wen-Chi Lin, Kang-Ping Lin

引用次数: 0

Analyzing differential regulatory networks modulated by continuous-state genomic features in glioblastoma multiforme 多形性胶质母细胞瘤中连续状态基因组特征调控的差异调控网络分析

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359676

Yu-Chiao Chiu, Kai-Wen Liang, T. Hsiao, Yidong Chen, E. Chuang

{"title":"Analyzing differential regulatory networks modulated by continuous-state genomic features in glioblastoma multiforme","authors":"Yu-Chiao Chiu, Kai-Wen Liang, T. Hsiao, Yidong Chen, E. Chuang","doi":"10.1109/BIBM.2015.7359676","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359676","url":null,"abstract":"Gene regulatory networks are a global representation of complex interactions between molecules that dictate cellular behavior. Study of a regulatory network modulated by single or multiple modulators' expression levels, including microRNAs (miRNAs) and transcription factors (TFs), in different conditions can further reveal the modulators' roles in diseases such as cancers. Existing computational methods for identifying such modulated regulatory networks are typically carried out by comparing groups of samples dichotomized with respect to the modulator status, ignoring the fact that most biological features are intrinsically continuous variables. Here we devised a sliding window-based regression scheme and proposed the Regression-based Inference of Modulation (RIM) algorithm to infer the dynamic gene regulation modulated by continuous-state modulators. We demonstrated the improvement in performance as well as computation efficiency achieved by RIM. Applying RIM to genome-wide expression profiles of 520 glioblastoma multiforme (GBM) tumors, we investigated miRNA- and TF-modulated gene regulatory networks and showed their association with dynamic cellular processes and brain-related functions in GBM. Overall, the proposed algorithm provides an efficient and robust scheme for comprehensively studying modulated gene regulatory networks.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134235419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Time series analysis of microbiome data regularized by local linear manifold 局部线性流形正则化微生物组数据的时间序列分析

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359666

Xingpeng Jiang, Xiaohua Hu, Tingting He

引用次数: 0

SOLOMON: An ontology for Sensory-Onset, Language-Onset and Motor-Onset dementias 索罗门:感觉性、语言性和运动性痴呆的本体论

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359814

M. Skarzynski, A. Craig, C. Taswell

引用次数: 6

Finding Frequent Approximate Subgraphs in medical image database 在医学图像数据库中寻找频繁近似子图

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359821

Linlin Gao, Haiwei Pan, Qilong Han, Xiaoqin Xie, Zhiqiang Zhang, Xiao Zhai, Pengyuan Li

引用次数: 6

Supporting HIV literature screening with data sampling and supervised learning 通过数据抽样和监督学习支持艾滋病文献筛选

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359733

Hayda Almeida, Marie-Jean Meurs, Leila Kosseim, A. Tsang

{"title":"Supporting HIV literature screening with data sampling and supervised learning","authors":"Hayda Almeida, Marie-Jean Meurs, Leila Kosseim, A. Tsang","doi":"10.1109/BIBM.2015.7359733","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359733","url":null,"abstract":"This paper presents a supervised learning approach to support the screening of HIV literature. The manual screening of biomedical literature is an important task in the process of systematic reviews. Researchers and curators have the very demanding, time-consuming and error-prone task of manually identifying documents that must be included in a systematic review concerning a specific problem. We implemented a supervised learning approach to support screening tasks, by automatically flagging potentially selected documents in a list retrieved by a literature database search. To overcome the main issues associated with the automatic literature screening task, we evaluated the use of data sampling, feature combinations, and feature selection methods, generating a total of 105 classification models. The models yielding best results were composed by the Logistic Model Trees classifier, a fairly balanced training set, and feature combination of Bag-Of-Words and MeSH terms. According to our results, the system correctly labels the great majority of relevant documents, and it could be used to support HIV systematic reviews to allow researchers to assess a greater number of documents in less time.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133425509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1