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

Identifying inorganic material affinity classes for peptide sequences based on context learning 基于上下文学习的肽序列无机物亲和类识别

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359742

Guangxu Xun, Xiaoyi Li, Marc R. Knecht, P. Prasad, M. Swihart, T. Walsh, A. Zhang

{"title":"Identifying inorganic material affinity classes for peptide sequences based on context learning","authors":"Guangxu Xun, Xiaoyi Li, Marc R. Knecht, P. Prasad, M. Swihart, T. Walsh, A. Zhang","doi":"10.1109/BIBM.2015.7359742","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359742","url":null,"abstract":"There is a growing interest in identifying inorganic material affinity classes for peptide sequences due to the development of bionanotechnology and its wide applications. In particular, a selective model capable of learning cross-material affinity patterns can help us design peptide sequences with desired binding selectivity for one inorganic material over another. However, as a newly emerging topic, there are several distinct challenges of it that limit the performance of many existing peptide sequence classification algorithms. In this paper, we propose a novel framework to identify affinity classes for peptide sequences across inorganic materials. After enlarging our dataset by simulating peptide sequences, we use a context learning based method to obtain the vector representation of each amino acid and each peptide sequence. By analyzing the structure and affinity class of each peptide sequence, we are able to capture the semantics of amino acids and peptide sequences in a vector space. At the last step we train our classifier based on these vector features and the heuristic rules. The construction of our models gives us the potential to overcome the challenges of this task and the empirical results show the effectiveness of our models.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"51 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":"115101456","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: 4

EEG-based seizure detection using discrete wavelet transform through full-level decomposition 基于脑电图的癫痫检测采用离散小波变换通过全水平分解

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359914

Duo-jin Chen, S. Wan, F. S. Bao

{"title":"EEG-based seizure detection using discrete wavelet transform through full-level decomposition","authors":"Duo-jin Chen, S. Wan, F. S. Bao","doi":"10.1109/BIBM.2015.7359914","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359914","url":null,"abstract":"Electroencephalogram (EEG) is a gold standard in epilepsy diagnosis and has been widely studied for epilepsy-related signal classification. In the past few years, discrete wavelet transform (DWT) has been widely used to analyze epileptic EEG. However, there are two practical questions unanswered: 1. what the best mother wavelet for epileptic EEG analysis is; 2. what the optimal level of wavelet decomposition is. The main challenge in using wavelet transform is selecting the optimal mother wavelet for the given task, as different mother wavelet applied on the same signal may produces different results. Such a problem also exist in epileptic EEG analysis based on wavelet. Deeper DWT can yield more detailed depiction of signals but it requires substantially more computational time. In this paper, we study these problems, using the most common epileptic EEG classification task, seizure detection, as an example. The results show that all 7 mother wavelets used in this work achieve high seizure detection accuracy at high decomposition levels. Also, decomposition level effects the detection accuracy more significantly than mother wavelets. For all wavelets, decomposition beyond level 7 improves accuracy limitedly and even decreases accuracy. We further study the most effective bands and features for seizure detection. An interpretation to our results is that seizure and non-seizure EEGs differ across all conventional frequency bands of human EEG rhythms. The best accuracy of seizure detection achieved in this research is 92.30% using coif3 from levels 2 to 7.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"46 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":"123166017","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: 12

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