2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)最新文献_第10页

Scalable, updatable predictive models for sequence data 可扩展的、可更新的序列数据预测模型

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706652

Neeraj Koul, N. Bui, Vasant G Honavar

{"title":"Scalable, updatable predictive models for sequence data","authors":"Neeraj Koul, N. Bui, Vasant G Honavar","doi":"10.1109/BIBM.2010.5706652","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706652","url":null,"abstract":"The emergence of data rich domains has led to an exponential growth in the size and number of data repositories, offering exciting opportunities to learn from the data using machine learning algorithms. In particular, sequence data is being made available at a rapid rate. In many applications, the learning algorithm may not have direct access to the entire dataset because of a variety of reasons such as massive data size or bandwidth limitation. In such settings, there is a need for techniques that can learn predictive models (e.g., classifiers) from large datasets without direct access to the data. We describe an approach to learn from massive sequence datasets using statistical queries. Specifically we show how Markov Models and Probabilistic Suffix Trees (PSTs) can be constructed from sequence databases that answer only a class of count queries. We analyze the query complexity (a measure of the number of queries needed) for constructing classifiers in such settings and outline some techniques to minimize the query complexity. We also show how some of the models can be updated in response to addition or deletion of subsets of sequences from the underlying sequence database.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133264480","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: 6

A possible mutation that enables H1N1 influenza a virus to escape antibody recognition 一种可能的突变使甲型H1N1流感病毒逃避抗体识别

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706541

C. Su, S. D. Handoko, C. Kwoh, C. Schönbach, X. Li

引用次数: 3

An automatic procedure to search highly repetitive sequences in genome as fluorescence in situ hybridization probes and its application on Brachypodium distachyon 荧光原位杂交探针在基因组高重复序列搜索中的应用

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706629

Qiwei Li, Tong Liang, Xiaodan Fan, Chunhui Xu, Weichang Yu, S. Li

引用次数: 1

Represented indicator measurement and corpus distillation on focus species detection 介绍了焦点物种检测中的指标测量和语料蒸馏

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706647

Chih-Hsuan Wei, Hung-Yu kao

引用次数: 2

Global analysis of miRNA target genes in colon rectal cancer 结肠癌miRNA靶基因的全局分析

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706588

M. Pradhan, L. Ledford, Yogesh Pandit, M. Palakal

引用次数: 2

Discovering negative correlated gene sets from integrative gene expression data for cancer prognosis 从综合基因表达数据中发现与癌症预后负相关的基因集

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706615

Tao Zeng, Xuan Guo, Juan Liu

{"title":"Discovering negative correlated gene sets from integrative gene expression data for cancer prognosis","authors":"Tao Zeng, Xuan Guo, Juan Liu","doi":"10.1109/BIBM.2010.5706615","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706615","url":null,"abstract":"Along with the emergence and development of translational biomedicine, more and more genetic information has been applied in clinical practice. In recent decade, the discovery of genetic biomarkers for cancer prognosis obtains increasing attentions and many methods have been developed. The ”element” methods use one or two independent genes to judge the Boolean status of disease. The ”set” methods use general genetic biomarkers to classify patients into different risks as a whole. And the advanced ”sets” methods use a group of different gene sets as biomarkers. However, the existing methods always concern positive correlations among genes ignoring negative correlations. Whereas the negative regulation, negative feedback, and functional repression are actually the important clues in cancer expression profiles. Therefore, in this paper, we propose to mine negative correlated gene sets (NCGSs) from multiple datasets, and use them along with the pure positive correlated gene sets for prognosis classification. The exploring experimental results have shown the encouraging promotion of cancer prognosis accuracy with NCGSs.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115429718","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

A relevance-novelty combined model for genomics search result diversification 基因组学搜索结果多样化的关联-新颖性组合模型

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706654

Xiaoshi Yin, Zhoujun Li, Xiangji Huang, Xiaohua Hu

引用次数: 2

Gene cluster profile vectors: A novel method to infer functional coupling using both gene proximity and co-occurrence profiles 基因簇谱载体:一种利用基因接近性和共现谱推断功能耦合的新方法

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706530

V. Pejaver, Sun Kim

{"title":"Gene cluster profile vectors: A novel method to infer functional coupling using both gene proximity and co-occurrence profiles","authors":"V. Pejaver, Sun Kim","doi":"10.1109/BIBM.2010.5706530","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706530","url":null,"abstract":"Proximity-based methods and co-evolution-based phylogenetic profiles methods have been successfully used for the identification of functionally related genes. Proximity-based methods are effective for physically clustered genes while the phylogenetic profiles method is effective for co-occurring gene sets. However, both methods predict many false positives and false negatives. In this paper, we propose the Gene Cluster Profile Vector (GCPV) method, which combines these two methods by using phylogenetic profiles of whole gene clusters. Moreover, the GCPV method is, currently, the only method that allows for the characterization of relationships between gene clusters themselves. The GCPV method groups together reasonably related operons in E. coli about 60% of the time. The method is minimally dependent on the reference genome set used and it outperforms the conventional phylogenetic profiles method. Finally, we show that the method works well for predicted gene clusters from C. crescentus and can serve as an important tool not only for understanding gene function, but also for elucidating mechanisms of general biological processes.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"168 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122196440","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

A gene ranking method using text-mining for the identification of disease related genes 基于文本挖掘的疾病相关基因排序方法

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706616

Hyungmin Lee, Miyoung Shin, Munpyo Hong

{"title":"A gene ranking method using text-mining for the identification of disease related genes","authors":"Hyungmin Lee, Miyoung Shin, Munpyo Hong","doi":"10.1109/BIBM.2010.5706616","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706616","url":null,"abstract":"For the identification of significant genes involved in specific diseases, microarray gene expression profiles have been widely used to prioritize candidate genes. In this paper, we propose a new gene ranking method that employs genegene relations extracted from literature along with gene expression scores obtained from microarrays. Here the genegene relations are extracted by taking a hybrid approach which is a combination of syntactic analysis and co-occurrence based approaches. Specifically, we perform the syntactic parsing on the text and then, within each clause of the parsed sentence, the co-occurred gene names are considered to be mutually related. Both the gene network derived from the gene-gene relations obtained in the above way and the gene expression scores are given as the inputs to the GeneRank algorithm. For the evaluation of our approach, we conducted experiments with the publicly available prostate cancer data. The results show that our method is superior in the precision and the recall to the original GeneRank which employs the gene-gene relations built from gene ontology annotations. Furthermore, our hybrid approach to the gene-gene relation extraction produces better prioritization of truly disease-related genes in top ranks than the existing popular co-occurrence approach.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114065648","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

GPU-based triangulation of the van der Waals surface 基于gpu的范德华曲面三角剖分

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706650

Sérgio Dias, A. Gomes

引用次数: 3