2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)最新文献

Overcoming drug resistance by co-targeting 通过共同靶向克服耐药性

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706562

M. Ayati, Golnaz Taheri, S. Arab, L. Wong, C. Eslahchi

{"title":"Overcoming drug resistance by co-targeting","authors":"M. Ayati, Golnaz Taheri, S. Arab, L. Wong, C. Eslahchi","doi":"10.1109/BIBM.2010.5706562","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706562","url":null,"abstract":"Removal or suppression of key proteins in an essential pathway of a pathogen is expected to disrupt the pathway and prohibit the pathogen from performing a vital function. Thus disconnecting multiple essential pathways should disrupt the survival of a pathogen even when it has multiple pathways to drug resistance. We consider a scenario where the drug-resistance pathways are unknown. To disrupt these pathways, we consider a cut set S of G, where G is a connected simple graph representing the protein interaction network of the pathogen, so that G-S splits to two partitions such that the endpoints of each pathway are in different partitions. If the difference between the sizes of the two partitions is high, the probability of existence of a functioning pathway in one partition is increased. Thus, we need to partition the graph into two balanced partitions. We approximate the balanced bipartitioning problem with spectral bipartitioning since finding (2,1)-separator is NP-complete. We test our technique on E. coli and C. jejuni. We show that over 50% of genes in the cut sets are essential. Moreover, all proteins in the cut sets have fundamental roles in cell and inhibition of each of them is harmful for cell survival. Also, 20% and 17% of known targets are in the vertex cut of E. coli and C. jejuni. Hence our approach has produced plausible “co-targets” whose inhibition should counter a pathogen's drug resistance.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"40 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":"123102193","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

Hierarchical decomposition of vessel skeletons for graph creation and feature extraction 用于图形创建和特征提取的血管骨架分层分解

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706609

K. Drechsler, C. O. Laura

引用次数: 22

Decomposing protein interactome networks by graph entropy 用图熵分解蛋白质相互作用组网络

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706633

Hao Lian, C. Song, Young-Rae Cho

{"title":"Decomposing protein interactome networks by graph entropy","authors":"Hao Lian, C. Song, Young-Rae Cho","doi":"10.1109/BIBM.2010.5706633","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706633","url":null,"abstract":"Recent high-throughput experimental methods have generated protein-protein interaction data in the genome scale, called interactome. Various graph clustering algorithms have been applied to the protein interactome networks for identifying protein complexes and predicting functional modules. Although the previous algorithms are scalable and robust, their accuracy is still limited because of complex connectivity of the networks. In this study, we propose a novel information-theoretic definition, Graph Entropy, as a measure of structural complexity of a graph. Loss of graph entropy represents an increase in modularity of the graph. Based on this concept, we present a graph clustering algorithm. Starting from a random seed vertex and its neighbors as a seed cluster, the algorithm iteratively adds or removes vertices on the border of the cluster to minimize graph entropy. We make an additional improvement on the algorithm for generating overlapping clusters. In the experiments with the yeast protein interactome network, we show the graph entropy-based approach has higher accuracy in predicting functional modules than other competing methods.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"63 4 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":"116259630","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

An Iterated Conditional Modes solution for sparse Bayesian factor modeling of transcriptional regulatory networks 转录调控网络稀疏贝叶斯因子建模的迭代条件模式解

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706587

Jia Meng, Jianqiu Zhang, Yidong Chen, Yufei Huang

引用次数: 0

Exploring a multi-source fusion approach for genomics information retrieval 探索基因组学信息检索的多源融合方法

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706649

Qinmin Hu, Xiangji Huang, Jun Miao

引用次数: 3

Prediction of low coverage prone regions for Illumina sequencing projects using a support vector machine 利用支持向量机预测Illumina测序项目的低覆盖易发区域

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706527

Zejun Zheng, B. Schmidt, G. Bourque

引用次数: 0

Utilizing Cox regression model to assess the relations between predefined gene sets and the survival outcome of lung adenocarcinoma 利用Cox回归模型评估预定义基因集与肺腺癌生存结局的关系

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706566

Jo-Yang Lu, E. Chuang, C. K. Hsiao, M. Tsai, L. Lai, Pei-Chun Chen

引用次数: 0

Concurrent analysis of copy number variations and expression profiles to identify genes associated with tumorigenesis and survival outcome in lung adenocarcinoma 拷贝数变异和表达谱的并发分析，以确定与肺腺癌肿瘤发生和生存结果相关的基因

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706578

T. Lu, L. Lai, C. K. Hsiao, Pei-Chun Chen, M. Tsai, E. Chuang

{"title":"Concurrent analysis of copy number variations and expression profiles to identify genes associated with tumorigenesis and survival outcome in lung adenocarcinoma","authors":"T. Lu, L. Lai, C. K. Hsiao, Pei-Chun Chen, M. Tsai, E. Chuang","doi":"10.1109/BIBM.2010.5706578","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706578","url":null,"abstract":"Lung cancer has been one of the major causes of cancer-related death worldwide. To predict survival outcomes of lung cancer patients, many prognosis gene sets were identified by using gene expression microarrays. However, these gene sets were often inconsistent across independent cohorts. To identify genes with more consistency, we combined gene expression and copy number variations (CNVs). Affymetrix SNP 6.0 and u133plus2.0 microarrays were performed on 42 pairs of lung adenocarcinoma patients. The copy number varied regions (CNVR) existed in more than 30% samples were identified and 475 differentially expressed genes with concordant changes were selected for pathway analysis. Thirteen pathways were significantly enriched among the 475 CNV-associated genes, and survival analyses showed these pathways had generally consistent and significant prediction probabilities across three independent microarray studies. Therefore, integration between gene expression and copy number may help to lower false discovery rate and identify genes used to predict survival outcomes.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"44 3 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":"128870111","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

Template-based scoring functions for visualizing biological insights of H-2Kb-peptide-TCR complexes 基于模板的评分功能，用于可视化h - 2kb肽- tcr复合物的生物学见解

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706550

I. Liu, Yu-Shu Lo, Jinn-Moon Yang

{"title":"Template-based scoring functions for visualizing biological insights of H-2Kb-peptide-TCR complexes","authors":"I. Liu, Yu-Shu Lo, Jinn-Moon Yang","doi":"10.1109/BIBM.2010.5706550","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706550","url":null,"abstract":"Class-I major histocompatibility complex (MHC), peptide, and T-cell receptor (TCR) play an essential role of adaptive immune responses. Many prediction servers are available for identification of peptides that bind to MHC class I molecules. These servers are often lack of detailed interacting residues and binding models for analyzing MHC-peptide-TCR interaction mechanisms. This study numerously enhanced the template-based scoring function derived from protein-protein interactions for identifying MHC-peptide-TCR binding models. The scoring function considers both the template similarity and interacting force to ensure the statistically significant interface similarity between the peptide candidates and structure templates. The result shows that our scoring function is comparative to the public websites for identifying MHC binding peptides. Our model, considering both the MHC-peptide and peptide-TCR interfaces, is able to provide visualization and the biological insights of MHC-peptide-TCR binding models. We believe that our model is useful for the development of peptide-based vaccines.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"44 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":"115235352","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

Accurate prediction of ATP-binding residues using sequence and sequence-derived structural descriptors 使用序列和序列衍生的结构描述子准确预测atp结合残基

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706533

Ke Chen, M. Mizianty, Lukasz Kurgan

引用次数: 0