2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)最新文献_第2页

Coupling Oriented Hidden Markov Random Field Model with Local Clustering for Segmenting Blood Vessels and Measuring Spatial Structures in Images of Tumor Microenvironment 面向耦合的局部聚类隐马尔可夫随机场模型用于肿瘤微环境图像血管分割和空间结构测量

2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW) Pub Date : 2011-11-12 DOI: 10.1109/BIBM.2011.104

Yanqiao Zhu, Fuhai Li, D. Cridebring, Jinwen Ma, Stephen T. C. Wong, T. Vadakkan, Mei Zhang, John D. Landua, Wei Wei, M. Dickinson, J. Rosen, M. Lewis

{"title":"Coupling Oriented Hidden Markov Random Field Model with Local Clustering for Segmenting Blood Vessels and Measuring Spatial Structures in Images of Tumor Microenvironment","authors":"Yanqiao Zhu, Fuhai Li, D. Cridebring, Jinwen Ma, Stephen T. C. Wong, T. Vadakkan, Mei Zhang, John D. Landua, Wei Wei, M. Dickinson, J. Rosen, M. Lewis","doi":"10.1109/BIBM.2011.104","DOIUrl":"https://doi.org/10.1109/BIBM.2011.104","url":null,"abstract":"Interactions between cancer cells and factors within the tumor microenvironment (mE) are essential for understanding tumor development. The spatial relationships between blood vessel cells and cancer cells, e.g. tumor initiating cells (TICs), are an important parameter. Accurate segmentation of blood vessel is necessary for the quantization of their spatial relationships. However, this remains an open problem due to uneven intensity and low signal to noise ratio (SNR). To overcome these challenges, we propose a novel approach that integrates an oriented hidden Markov random field model (Ori-HMRF) with local clustering. The local clustering delineates boundaries of blood vessel segments with low SNR. Then blood vessel segments are viewed as random variables in the Ori-HMRF and their spatial dependence is defined based on directional information. The Ori-HMRF model suppresses noise and generates accurate blood vessel segmentation results. Experimental validations were conducted on both normal mammary and breast cancer tissues.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"1 1","pages":"352-357"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80148122","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: 8

Assessments of intra- and inter-host diversity of hepatitis C virus using Next Generation Sequencing and Mass spectrometry 使用下一代测序和质谱法评估丙型肝炎病毒宿主内和宿主间的多样性

2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW) Pub Date : 2011-11-12 DOI: 10.1109/BIBMW.2011.6112358

Zoya Dimitrova, David S. Campo, S. Ramachandran, Gilberto Vaughan, L. Ganova-Raeva, Yulin Lin, J. Forbi, G. Xia, P. Skums, B. Pearlman, Y. Khudyakov

{"title":"Assessments of intra- and inter-host diversity of hepatitis C virus using Next Generation Sequencing and Mass spectrometry","authors":"Zoya Dimitrova, David S. Campo, S. Ramachandran, Gilberto Vaughan, L. Ganova-Raeva, Yulin Lin, J. Forbi, G. Xia, P. Skums, B. Pearlman, Y. Khudyakov","doi":"10.1109/BIBMW.2011.6112358","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112358","url":null,"abstract":"Recent advances in sequencing methods allow the analysis of an unprecedented number of viral variants from infected patients and present a novel opportunity for understanding viral evolution, drug resistance and immune escape. In the present paper, we compared three technologies for amplicon analysis: (i) Next Generation Sequencing; (ii) Clonal sequencing using End-point Limiting-dilution for isolation of individual sequence variants followed by Real-Time PCR and sequencing; and (iii) Mass spectrometry of base-specific cleavage reactions of a target sequence. Hypervariable region 1 of hepatitis C virus was analyzed using these three technologies to assess diversity and genetic relatedness of intra-host viral populations in specimens obtained from 38 patients. Estimates of population heterogeneity varied among technologies. However, all three technologies were equally accurate in identification of genetic relatedness among viral strains, supporting their application in molecular epidemiology for tracking viral variants and detecting transmission events.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"30 1","pages":"79-86"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85015613","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

Rapid identification of multi-PTMs peptide sequence tags with a graph search approach 基于图搜索方法的多ptms肽序列标签快速识别

2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW) Pub Date : 2011-11-12 DOI: 10.1109/BIBMW.2011.6112382

Hui Li, Chunmei Liu, M. Rwebangira, L. Burge, W. Southerland

引用次数: 0

A symmetry-driven BP algorithm for the Discretizable Molecular Distance Geometry Problem 可离散分子距离几何问题的对称驱动BP算法

2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW) Pub Date : 2011-11-12 DOI: 10.1109/BIBMW.2011.6112403

A. Mucherino, C. Lavor, Leo Liberti

引用次数: 9

imiRTP: An Integrated Method to Identifying miRNA-target Interactions in Arabidopsis thaliana imiRTP:一种鉴定拟南芥mirna -靶标相互作用的综合方法

2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW) Pub Date : 2011-11-12 DOI: 10.1109/BIBM.2011.13

Jiandong Ding, Shusi Yu, U. Ohler, J. Guan, Shuigeng Zhou

引用次数: 6

Integration of polygenic and individual SNP effects in genome-wide association analyses 在全基因组关联分析中整合多基因和个体SNP效应

2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW) Pub Date : 2011-11-12 DOI: 10.1109/BIBMW.2011.6112531

Nick V. L. Serão, J. Beever, Dan B. Faulkner, S. Rodriguez-Zas

{"title":"Integration of polygenic and individual SNP effects in genome-wide association analyses","authors":"Nick V. L. Serão, J. Beever, Dan B. Faulkner, S. Rodriguez-Zas","doi":"10.1109/BIBMW.2011.6112531","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112531","url":null,"abstract":"The lack of consideration of polygenic effects in genome-wide association studies (GWAS) may bias the results in complex traits controlled by multiple genes. The goal of this study is to develop a composite-GWAS model that identifies individual SNPs while adjusting for polygenic effects. The complex trait residual feed intake (RFI), an indicator of the feed efficiency based on maintenance and growth, was modeled. RFI and genotypic data (5,910 SNPs from chromosomes 3, 11 and 24) from 1,387 steers from different breeds and receiving different diets were analyzed, with and without the additive polygenic effect. The model included the fixed effects of days of feed, diet, breed and interaction between diet and breed, and the random effects of contemporary group and additive polygenic effect. A total of 69 and 141 SNPs were detected (P-value < 0.01) with the model including and excluding polygenic effects, respectively. The higher number of SNPs identified by the second model confirms that ignoring polygenic effects in GWAS of multi-gene traits can lead to false positives due to linkage disequilibrium. Seven SNPs (P-value < 0.001), four in chromosomes 3, two in chromosome 11 and one in chromosome 24, were detected using the polygenic model. Two SNPs, one from chromosome 3 and one from 11 are located within coding gene regions. Our results demonstrate the need to use composite-GWAS that include polygenic effects in complex multi-gene traits. These results indicated that the genetic improvement of feed efficiency in beef cattle may be accelerated by the incorporation of these markers in genomic selection strategies.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"88 1","pages":"985-987"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84179001","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

Identifying Ovarian Cancer Chemotherapy Response Relevant Gene Cliques 卵巢癌化疗反应相关基因群的鉴定

2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW) Pub Date : 2011-11-12 DOI: 10.1109/BIBM.2011.65

Yan-E. Li, Juan Zhang, Bin Han, Lihua Li

{"title":"Identifying Ovarian Cancer Chemotherapy Response Relevant Gene Cliques","authors":"Yan-E. Li, Juan Zhang, Bin Han, Lihua Li","doi":"10.1109/BIBM.2011.65","DOIUrl":"https://doi.org/10.1109/BIBM.2011.65","url":null,"abstract":"Operation with adjuvant chemotherapy is still the principal means to treat Ovarian cancer. Identifying Ovarian Cancer Chemotherapy Response (OCCR) relevant genes and describe their interactions is thus an important issue. However the problems of high dimensional micro array data and the scarcity of biological priors make building a complete OCCR biological network intractable. To this end, we combine liquid association (LA) algorithm with biological knowledgebase searching to identify OCCR relevant gene clique and describe their interactions. Rather than trying to build a gene network, our approach focus on identifying OCCR relevant gene cliques and then patching them up. Statistical analysis and biological validation show that the identified gene cliques play important roles in tumor genesis, immunity, cells proliferation and migration etc and significantly OCCR relevant. More importantly, the connection of independent gene cliques is established and the associations of genes are described. Methodologically, the proposed method avoids the problem of complex computation, relies only on available biological priors and provides a novel way to build gene network.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"51 1","pages":"294-298"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77817478","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: 0

Decision Support in Safety Intelligence Using Pharmaconnect Knowledgebase 基于Pharmaconnect知识库的安全智能决策支持

2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW) Pub Date : 2011-11-12 DOI: 10.1109/BIBM.2011.135

S. Matis, Matt Clark, Marcus Bjäreland, Brian Takasaki, N. Mian, S. Muresan, Sorona Popa

{"title":"Decision Support in Safety Intelligence Using Pharmaconnect Knowledgebase","authors":"S. Matis, Matt Clark, Marcus Bjäreland, Brian Takasaki, N. Mian, S. Muresan, Sorona Popa","doi":"10.1109/BIBM.2011.135","DOIUrl":"https://doi.org/10.1109/BIBM.2011.135","url":null,"abstract":"On average it takes approximately 13 years and over 1 billion dollars to take a new medical entity (NME) from a concept to an approved product. As high as 40% of the drugs fail to make it thru to market at a significant cost and even with the best of preclinical testing some do cause unexpected or “idiosyncratic” toxicity. In order to reduce the length of the development timeline, support problem solving, provide improved decision making and reduce late stage attrition, many pharmaceutical companies are developing methodology to predict safety issues earlier in this process. While the exchange of scientific knowledge is still primarily thru the published medical literature, advances in semantic technology have dramatically changed the way a scientist accesses data and information. In particular, ontology development coupled with natural language processing, have made a huge impact on the review of the scientific literature and extraction of data from internal and external sources. Creation of highly linked knowledge bases enables the development of predictive methods as well as supports problem solving. These activities have been shown to be highly useful in reducing time and effort. The Knowledge Engineering initiative within AstraZeneca has recently delivered the first version of a knowledgebase that integrates internal and external evidence for connections between key concepts such as targets, pathways, compounds, diseases, preclinical, and clinical outcome from Chemistry, Competitive, Disease and Safety Intelligence workstreams. This talk will describe the system; demonstrate the impact of this new platform with specific examples from Safety, and discuss lessons learned during its development. 2011 IEEE International Conference on Bioinformatics and Biomedicine 978-0-7695-4574-5/11 $26.00 © 2011 IEEE DOI 10.1109/BIBM.2011.135 661","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"119 1","pages":"661-661"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77946198","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: 0

Lessons learned in improving the adoption of a real-time NLP decision support system 改进采用实时自然语言处理决策支持系统的经验教训

2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW) Pub Date : 2011-11-12 DOI: 10.1109/BIBMW.2011.6112446

Yang Huang, D. Zisook, Yunan Chen, M. Selter, P. Minardi, J. Mattison

引用次数: 0

Reconstruction of gene regulatory networks by stepwise multiple linear regression from time-series microarray data 基于时间序列微阵列数据的逐步多元线性回归基因调控网络重构

2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW) Pub Date : 2011-11-12 DOI: 10.1109/BIBMW.2011.6112544

Yiqian Zhou, Jacqueline Gerhart, A. Sacan

{"title":"Reconstruction of gene regulatory networks by stepwise multiple linear regression from time-series microarray data","authors":"Yiqian Zhou, Jacqueline Gerhart, A. Sacan","doi":"10.1109/BIBMW.2011.6112544","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112544","url":null,"abstract":"Gene regulatory networks provide a powerful abstraction of the complex interactions among genes involved in functional pathways. Experimental determination of these interactions using a classical experimental method, although of extreme value, is laborious and prohibitive at large scales. Over the last decade, a number of computational approaches have been developed to infer gene regulatory networks from high-throughput experimental data. In this study, we introduce a new algorithm for regulatory network inference, based on stepwise multiple regression of time-series microarray data. Compared to other existing methods, our regression-based method provides a clear interpretation of the inferred interactions. The statistical significance associated with each prediction can be utilized to rank the interactions, which is important in prioritization of predictions for further experimental verification. We demonstrate the performance of our approach on a well-known yeast cell cycle pathway and show that it makes more accurate predictions than existing methods.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"35 1","pages":"1017-1019"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81348306","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