2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)最新文献_第8页

Intracranial pressure level prediction in traumatic brain injury by extracting features from multiple sources and using machine learning methods 基于多源特征提取和机器学习方法的颅内压水平预测

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706619

Wenan Chen, Charles Cockrell, Kevin Ward, K. Najarian

引用次数: 24

A generalized sequence pattern matching algorithm using complementary dual-seeding 一种基于互补双播种的广义序列模式匹配算法

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706593

Bing Ni, Leung-Yau Lo, K. Leung

{"title":"A generalized sequence pattern matching algorithm using complementary dual-seeding","authors":"Bing Ni, Leung-Yau Lo, K. Leung","doi":"10.1109/BIBM.2010.5706593","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706593","url":null,"abstract":"In this work, we define generalized (sequence) patterns, which is based on several real Biological problems, including transcription factors (TFs) binding to transcription factor binding sites (TFBSs), cis-regulatory modules, protein domain analysis, and alternative splicing etc. Simply speaking, a generalized pattern is composed of several substrings with gaps in-between two substrings. We propose a generalized pattern matching algorithm that uses a complementary dualseeding strategy, which is sensitive to errors (both mismatches and indels). We also develop a generalized pattern matching tool1, which is to our knowledge the first ever developed specially for generalized pattern matching. Rather than replacing the existing general purpose matching tools, such as BLAST, BLAT, and PatternHunter etc, our tool provides an alternative and helps users to solve real problems, especially those that can be modeled as generalized patterns. We use data randomly sampled from reference sequences of human genome (NCBI build v18) in experiments, and hit 98.74% generalized patterns on average. The tool runs on both LINUX and Windows platforms, and the memory peak goes to a little bit larger than 1GB only.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"89 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":"125001716","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

Gene expression rule discovery with a multi-objective neural-genetic hybrid 基于多目标神经遗传杂交的基因表达规则发现

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706646

E. Keedwell, A. Narayanan

引用次数: 0

MiRNAs as promising phylogenetic markers for inferring deep metazoan phylogeny and in support of Olfactores hypothesis mirna作为推测深层后生动物系统发育和支持嗅觉假说的有前途的系统发育标记

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706545

Q. Cai, Xiaoyan Zhang, Zuofeng Li

引用次数: 2

IsoKEGG: A logic based system for querying biological pathways in KEGG IsoKEGG:一个基于逻辑的系统，用于查询KEGG中的生物途径

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706642

Kazi Zakia Sultana, Anupam Bhattacharjee, H. Jamil

{"title":"IsoKEGG: A logic based system for querying biological pathways in KEGG","authors":"Kazi Zakia Sultana, Anupam Bhattacharjee, H. Jamil","doi":"10.1109/BIBM.2010.5706642","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706642","url":null,"abstract":"Understanding the interaction patterns among a set of biological entities in a pathway is an important exercise because it potentially could reveal the role of the entities in biological systems. Although a considerable amount of effort has been directed to the detection and mining of patterns in biological pathways in contemporary research, querying biological pathways remained relatively unexplored. Querying is principally different in which we retrieve pathways that satisfy a given property in terms of its topology, or constituents. One such property is subnetwork matching using various constituent parameters. In this paper, we introduce a logic based framework for querying biological pathways based on a novel and generic subgraph isomorphism computation technique. We cast this technique into a graphical interface called IsoKEGG to facilitate flexible querying of KEGG pathways. We demonstrate that IsoKEGG is flexible enough to allow querying based on isomorphic pathway topologies as well as matching any combination of node names, types, and edges. It also allows editing KGML represented query pathways and returns all possible pathways in KEGG that satisfy a given query condition that the users are able to investigate further.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"8 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":"125331086","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

Classification of genome-wide copy number variations and their associated SNP and gene networks analysis 全基因组拷贝数变异的分类及其相关SNP和基因网络分析

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706526

Yang Liu, Yiu-Fai Lee, M. Ng

{"title":"Classification of genome-wide copy number variations and their associated SNP and gene networks analysis","authors":"Yang Liu, Yiu-Fai Lee, M. Ng","doi":"10.1109/BIBM.2010.5706526","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706526","url":null,"abstract":"Detection of genomic DNA copy number variations (CNVs) can provide a complete and more comprehensive view of human disease. In this paper, we incorporate DNA copy number variation data derived from SNP arrays into a computational shrunken model and formalize the detection of copy number variations as a case-control classification problem. By shrinkage, the number of relevant CNVs to disease can be determined. In order to understand relevant CNVs, we study their corresponding SNPs in the genome and find out the unique genes that those SNPs are located in. A gene-gene similarity value is computed using GOSemSim and gene pairs that has a similarity value being greater than a threshold are selected to construct several groups of genes. For the SNPs that involved in these groups of genes, a statistical software PLINK is employed to compute the pair-wise SNP-SNP interactions, and identify SNP networks based on their p-values. By using two real genome-wide data sets, we further demonstrate SNP and gene networks play a role in the biological process. An analysis shows that such networks have relationships directly or indirectly to disease study.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"102 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":"116125542","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

Improving robustness of gene ranking by resampling and permutation based score correction and normalization 通过重采样和基于排列的分数校正和归一化提高基因排序的稳健性

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706607

Feng Yang, K. Mao

引用次数: 6

Protein-protein interaction prediction using desolvation energies and interface properties 利用脱溶能和界面性质预测蛋白质-蛋白质相互作用

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706528

L. Rueda, Sridip Banerjee, Md. Mominul Aziz, Mohammad Raza

{"title":"Protein-protein interaction prediction using desolvation energies and interface properties","authors":"L. Rueda, Sridip Banerjee, Md. Mominul Aziz, Mohammad Raza","doi":"10.1109/BIBM.2010.5706528","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706528","url":null,"abstract":"An important aspect in understanding and classifying protein-protein interactions (PPI) is to analyze their interfaces in order to distinguish between transient and obligate complexes. We propose a classification approach to discriminate between these two types of complexes. Our approach has two important aspects. First, we have used desolvation energies — amino acid and atom type — of the residues present in the interface, which are the input features of the classifiers. Principal components of the data were found and then the classification is performed via linear dimensionality reduction (LDR) methods. Second, we have investigated various interface properties of these interactions. From the analysis of protein quaternary structures, physicochemical properties are treated as the input features of the classifiers. Various features are extracted from each complex, and the classification is performed via different linear dimensionality reduction (LDR) methods. The results on standard benchmarks of transient and obligate protein complexes show that (i) desolvation energies are better discriminants than solvent accessibility and conservation properties, among others, and (ii) the proposed approach outperforms previous solvent accessible area based approaches using support vector machines.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"1 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":"130236729","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: 16

Prediction of DNA-binding protein based on alpha shape modeling 基于α形状模型的dna结合蛋白预测

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706529

Weiqiang Zhou, Hong Yan

引用次数: 3

Fuzzy C-means method with empirical mode decomposition for clustering microarray data 基于经验模态分解的模糊c均值聚类方法

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706561

Yanfei Wang, Zuguo Yu, V. Anh

{"title":"Fuzzy C-means method with empirical mode decomposition for clustering microarray data","authors":"Yanfei Wang, Zuguo Yu, V. Anh","doi":"10.1109/BIBM.2010.5706561","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706561","url":null,"abstract":"Microarray techniques have revolutionized genomic research by making it possible to monitor the expression of thousands of genes in parallel. Data clustering analysis has been extensively applied to extract information from gene expression profiles obtained with DNA microarrays. Existing clustering approaches, mainly developed in computer science, have been adapted to microarray data. Among these approaches, fuzzy C-means (FCM) method is an efficient one. However, microarray data contains noise and the noise would affect clustering results. Some clustering structure still can be found from random data without any biological significance. In this paper, we propose to combine the FCM method with the empirical mode decomposition (EMD) for clustering microarray data in order to reduce the effect of the noise. We call this method fuzzy C-means method with empirical mode decomposition (FCM-EMD). Using the FCM-EMD method on gene microarray data, we obtained better results than those using FCM only. The results suggest the clustering structures of denoised data are more reasonable and genes have tighter association with their clusters. Denoised gene data without any biological information contains no cluster structure. We find that we can avoid estimating the fuzzy parameter m in some degree by analyzing denoised microarray data. This makes clustering more efficient. Using the FCM-EMD method to analyze gene microarray data can save time and obtain more reasonable results.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"18 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":"132080575","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: 15