2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology最新文献_第3页

Using a Bayesian Posterior Density in the Design of Perturbation Experiments for Network Reconstruction 基于贝叶斯后验密度的网络重构扰动实验设计

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594920

A. Almudevar, P. Salzman

引用次数: 9

An Efficient Approach to Detect a Protein Community from a Seed 一种从种子中检测蛋白质群落的有效方法

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594909

D. Wu, Xiaohua Hu

引用次数: 24

Heuristic Algorithm for Computing Reversal Distance with MultiGene Families via Binary Integer Programming 基于二进制整数规划的多基因家族反转距离计算启发式算法

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594916

J. Suksawatchon, C. Lursinsap, M. Bodén

引用次数: 2

Biological Sequence Prediction using General Fuzzy Automata 基于通用模糊自动机的生物序列预测

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594947

M. Doostfatemeh, S. C. Kremer

引用次数: 2

Reverse Engineering Non-Linear Gene Regulatory Networks Based on the Bacteriophage λ cI Circuit 基于噬菌体的逆向工程非线性基因调控网络&#955cI电路

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594936

J. Supper, C. Spieth, A. Zell

引用次数: 3

Pathway Analyst Automated Metabolic Pathway Prediction 途径分析自动化代谢途径预测

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594924

L. Pireddu, B. Poulin, D. Szafron, P. Lu, D. Wishart

{"title":"Pathway Analyst Automated Metabolic Pathway Prediction","authors":"L. Pireddu, B. Poulin, D. Szafron, P. Lu, D. Wishart","doi":"10.1109/CIBCB.2005.1594924","DOIUrl":"https://doi.org/10.1109/CIBCB.2005.1594924","url":null,"abstract":"Metabolic pathways are crucial to our understanding of biology. The speed at which new organisms are being sequenced is outstripping our ability to experimentally determine their metabolic pathway information. In recent years several initiatives have been successful in automating the annotations of individual proteins in these organisms, either experimentally or by prediction. However, to leverage the success of metabolic pathways we need to automate their identification in our rapidly growing list of sequenced organisms. We present a prototype system for predicting the catalysts of important reactions and for organizing the predicted catalysts and reactions into previously defined metabolic pathways. We compare a variety of predictors that incorporate sequence similarity (BLAST), hidden Markov models (HMM) and Support Vector Machines (SVM). We found that there is an advantage to using different predictors for different reactions. We validate our prototype on 10 metabolic pathways across 13 organisms for which we obtained a cross-validation precision of 71.5% and recall of 91.5% in predicting the catalyst proteins of all reactions.","PeriodicalId":330810,"journal":{"name":"2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129447228","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

Hypermotifs: Novel Discriminatory Patterns for Nucleotide Sequences and their Application to Core Promoter Prediction in Eukaryotes 高基序:核苷酸序列的新区分模式及其在真核生物核心启动子预测中的应用

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594949

C. Pridgeon, D. Corne

{"title":"Hypermotifs: Novel Discriminatory Patterns for Nucleotide Sequences and their Application to Core Promoter Prediction in Eukaryotes","authors":"C. Pridgeon, D. Corne","doi":"10.1109/CIBCB.2005.1594949","DOIUrl":"https://doi.org/10.1109/CIBCB.2005.1594949","url":null,"abstract":"We approach the general problem of finding a model that discriminates between classes of nucleotide sequences. In this area, a common approach is to train a model, such as a neural network, or a hidden Markov model, to perform the discrimination, using as inputs either the raw sequences encoded in a standard form, or features derived from the raw data in a pre-processing stage. In this paper a novel discriminatory pattern structure for nucleotide sequences is introduced, called a hypermotif, and evolutionary computation is used to evolve a collection of specific hypermotifs which discriminate between classes in the data. The raw nucleotide data are then processed, transforming it into feature vectors, where the features are the individual scores on the evolved hypermotifs. Using this transformation, any classification method may then be used to build an accurate predictive model. The approach is tested on a database of eukaryotic promoters, and find that this method enables us to outperform a standard multilayer perceptron (despite using a linear discriminant as the final classifier), and provides similar performance to the best approach so far for these data (which uses a time delay neural network)","PeriodicalId":330810,"journal":{"name":"2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129550114","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

A Hybrid CI-Based Knowledge Discovery System on Microarray Gene Expression Data 基于混合ci的微阵列基因表达数据知识发现系统

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594894

Yuchun Tang, Yuanchen He, Yanqing Zhang, Zhen Huang, Xiaohua Hu, Rajshekhar Sunderraman

{"title":"A Hybrid CI-Based Knowledge Discovery System on Microarray Gene Expression Data","authors":"Yuchun Tang, Yuanchen He, Yanqing Zhang, Zhen Huang, Xiaohua Hu, Rajshekhar Sunderraman","doi":"10.1109/CIBCB.2005.1594894","DOIUrl":"https://doi.org/10.1109/CIBCB.2005.1594894","url":null,"abstract":"A hybrid Computational Intelligence-based Knowledge Discovery system is presented in this paper. The system works in three phases. In phase 1, many feature selection algorithms are utilized to select informative cancer-related genes from microarray expression data. Compared with other algorithms, our GSVM-RFE algorithm demonstrates superior performance on the microarray expression dataset for AML/ALL classification. Specifically, a compact “ perfect” gene subset is reported. In phase 2, many intelligent computation models are implemented to extract useful knowledge about functions of selected genes to regulate the cancer being studied. Knowledge can ease further biomedical study because of reliable information sources, high prediction accuracy, and easiness to interpret. Currently, knowledge is represented in two formats, Web-based and Rule-based. As a future work, we plan to implement knowledge fusion algorithms in phase 3 to synthesize and consolidate hybrid knowledge into a single knowledge base to provide effective and efficient decision support for cancer diagnosis and drug discovery.","PeriodicalId":330810,"journal":{"name":"2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121221362","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

Depth Annotation of RNA Folds for Secondary Structure Motif Search 用于二级结构基序搜索的RNA折叠深度标注

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594896

D. Ashlock, J. Schonfeld

{"title":"Depth Annotation of RNA Folds for Secondary Structure Motif Search","authors":"D. Ashlock, J. Schonfeld","doi":"10.1109/CIBCB.2005.1594896","DOIUrl":"https://doi.org/10.1109/CIBCB.2005.1594896","url":null,"abstract":"The biological activity of RNA depends on the way it folds into secondary structures. Presented here is a framework for exploratory motif searching in the space of RNA secondary structures. A collection of RNA sequences, suspected of having a particular biological activity, is fragmented into overlapping pieces of a uniform size. Each piece is folded and the details of the fold are used to annotate the primary structure. Distances between annotated structures are computed. The distance matrix for the structures is then projected into the Euclidean plane for visualization and detection of clusters. A motif is taken to be a cluster in the two dimensional space. An instance of the framework is implemented for testing on a data set containing examples of the Iron Response Element in the following manner. Folding is performed with the Mfold package. A depth-of-fold that records stems and loops onto the primary sequence is used to annotate the pieces of RNA. Dynamic programming is used to find distances between pieces of annotated primary sequence. An evolutionary algorithm is then used to find a one-to-one mapping of pieces of RNA to points in the plane that has acceptable distortion of the distances found with dynamic programming. This one-to-one mapping is a form of non-linear projection that optimizes for fidelity of projected distances to the distances derived from the Iron Response Element data set.","PeriodicalId":330810,"journal":{"name":"2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127147254","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: 11

Selected String Representation for Whole Genomes 全基因组的选择字符串表示

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594905

Xiaomeng Wu, Guohui Lin

引用次数: 1