2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology最新文献_第2页

Intrinsic Disorder and Prote in Modifications: Building an SVM Predictor for Methylation 修饰中的内在紊乱和蛋白质:建立甲基化的支持向量机预测器

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594957

Kenneth Daily, P. Radivojac, A. Dunker

引用次数: 52

Selection Of Genetically Diverse Recombinant Inbreds With An Ordered Gene Evolutionary Algorithm 用有序基因进化算法选择遗传多样性重组自交系

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594923

D. Ashlock, Ruth Swanson, P. Schnable

{"title":"Selection Of Genetically Diverse Recombinant Inbreds With An Ordered Gene Evolutionary Algorithm","authors":"D. Ashlock, Ruth Swanson, P. Schnable","doi":"10.1109/CIBCB.2005.1594923","DOIUrl":"https://doi.org/10.1109/CIBCB.2005.1594923","url":null,"abstract":"Recombinant inbreds are created by crossing two genetically distinct inbred lines and then inbreeding the resulting progeny multiple times. They are used to estimate associations of genes by co-inheritance of alleles from the two parent inbred types in the recombinant inbreds derived from the cross in a process called genetic mapping. Typically the recombinant inbred lines used in a genetic mapping study are relatively well studied and so they are natural choices for microarray, proteomic, and metabolomic studies. These are quite costly and so typically use fewer individuals than are used in most genetic mapping studies. An evolutionary algorithm for selecting a subset of a collection of recombinant inbred lines with maximum genetic diversity in their mapping characters is described. The evolutionary algorithm is an ordered-gene algorithm with the first k genes in the ordered selection taken to be the subset. Ordered genes are a convenient representation for subset selection. It is found that the problem is not difficult and that in a well mixed mapping population of recombinant inbreds the marginal increase in diversity obtained by evolutionary optimization is small but significant. In order to better understand the problem, synthetic data are also examined and suggest that the problem is easy in general, not only in the specific biological cases used. Recombinant inbreds are created by crossing two genetically distinct inbred lines and then inbreeding the resulting progeny multiple times.","PeriodicalId":330810,"journal":{"name":"2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"21 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":"123647081","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

Evolutionary Granular Kernel Trees and Applications in Drug Activity Comparisons 进化颗粒核树及其在药物活性比较中的应用

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594907

Bo Jin, Yanqing Zhang, Binghe Wang

{"title":"Evolutionary Granular Kernel Trees and Applications in Drug Activity Comparisons","authors":"Bo Jin, Yanqing Zhang, Binghe Wang","doi":"10.1109/CIBCB.2005.1594907","DOIUrl":"https://doi.org/10.1109/CIBCB.2005.1594907","url":null,"abstract":"Kernel methods, specifically support vector machines (SVMs), have been widely used in many fields for data classification and pattern recognition. The performance of SVMs is mainly affected by kernel functions. With the growing interest of biological data prediction and chemical data prediction such as structure-property based molecule comparison, protein structure prediction and long DNA sequence comparison, more powerful and flexible kernels need to be designed in order effectively to express the prior knowledge and relationships within each data item. In this paper, the granular kernel concept is presented and related properties are described in detail. A hierarchical kernel design method is proposed to construct granular kernel trees (GKTs). For a particular problem, genetic algorithms (GAs) are used to find the optimum parameter settings of GKTs. In applications, SVMs with new kernel trees are employed for the comparisons of drug activities. The experimental results show that SVMs with GKTs and evolutionary GKTs can achieve better performances than SVMs with traditional RBF kernels in terms of prediction accuracy.","PeriodicalId":330810,"journal":{"name":"2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"8 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":"131128708","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: 7

Predicting Single Genes Related to Immune-Relevant Processes 预测与免疫相关过程相关的单基因

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594955

C. Spieth, F. Streichert, N. Speer, C. Sinzger, Kathrin Eberhard, A. Zell

引用次数: 3

Biclustering of Gene Expression Data Using Genetic Algorithm 基于遗传算法的基因表达数据双聚类

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594893

Anupam Chakraborty, Hitashyam Maka

引用次数: 68

Multi-Class Protein Subcellular Localization Prediction using Support Vector Machines 基于支持向量机的多类蛋白质亚细胞定位预测

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594964

Peng Wai Meng, Jagath Rajapakse

{"title":"Multi-Class Protein Subcellular Localization Prediction using Support Vector Machines","authors":"Peng Wai Meng, Jagath Rajapakse","doi":"10.1109/CIBCB.2005.1594964","DOIUrl":"https://doi.org/10.1109/CIBCB.2005.1594964","url":null,"abstract":"Prediction of protein subcellular localization from amino acid sequence is an important step towards elucidating the function of a protein. Here, we present an approach for predicting protein subcellular localizations from eukaryotic sequences using Support Vector Machines. Apart from using amino acid compositions, our prediction approach also considers biochemical characteristics of amino acids and their distribution patterns along the primary sequence of the query proteins. Consequently, improved predictive accuracy has been achieved on the Reinhardt and Hubbard’s dataset. For the four subcellular localizations of eukaryotic proteins, the total prediction accuracy obtained using the “ leave-one-out” cross-validation test is 88.88%. To the best of our knowledge, our approach obtained by far the best prediction accuracy for mitochondrial proteins, which are notoriously difficult to predict among eukaryotic proteins. Performance comparison results also showed that our approach outperformed existing protein subcellular localization prediction methods based solely on amino acid composition.","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":"129290266","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

Feedback Memetic Algorithms for Modeling Gene Regulatory Networks 基因调控网络建模的反馈模因算法

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594899

C. Spieth, F. Streichert, J. Supper, N. Speer, A. Zell

{"title":"Feedback Memetic Algorithms for Modeling Gene Regulatory Networks","authors":"C. Spieth, F. Streichert, J. Supper, N. Speer, A. Zell","doi":"10.1109/CIBCB.2005.1594899","DOIUrl":"https://doi.org/10.1109/CIBCB.2005.1594899","url":null,"abstract":"In this paper we address the problem of finding gene regulatory networks from experimental DNA microarray data. We focus on the evaluation of the performance of memetic algorithms on the inference problem. These algorithms are used to evolve an underlying quantitative mathematical model. The dynamics of the regulatory system are modeled with two commonly used approaches, namely linear weight matrices and S-systems. Due to the complexity of the inference problem, some researchers suggested evolutionary algorithms for this purpose. We introduce memetic enhancements to this optimization process to infer the parameters of sparsely connected nonlinear systems from the observed data. Due to the limited number of available data, the inferring problem is underdetermined and ambiguous. Further on, the problem often is multimodal and therefore appropriate optimization strategies become necessary. We propose a memetic method, which separates the overall inference problem into two subproblems to find the correct network: first, the search for a valid topology, and secondly, the optimization of the parameters of the mathematical model. The performance and the properties of the proposed methods are evaluated and compared to standard algorithms found in the literature.","PeriodicalId":330810,"journal":{"name":"2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"83 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":"131820273","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: 20

Multiple Sequence Alignment Containing a Sequence of Regular Expressions 包含正则表达式序列的多序列对齐

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594922

Abdullah N. Arslan

{"title":"Multiple Sequence Alignment Containing a Sequence of Regular Expressions","authors":"Abdullah N. Arslan","doi":"10.1109/CIBCB.2005.1594922","DOIUrl":"https://doi.org/10.1109/CIBCB.2005.1594922","url":null,"abstract":"A classical algorithm for the pairwise sequence alignment is the Smith Waterman algorithm which uses dynamic programming. The algorithm computes the maximum score of alignments that use insertions, deletions, and substitutions, with no consideration given in composition of the alignments. However, biologists favor applying their knowledge about common structures or functions into the alignment process. For alignment of protein sequences, several methods have been suggested for taking into account the motifs (a restricted regular expression) from the PROSITE database to guide alignments. One method modifies the Smith Waterman dynamic programming solution to reward alignments that contain matching motifs. Another method introduces the regular expression constrained sequence alignment problem in which pairwise alignments are constrained to contain a given regular expression. This latter method constructs a weighted finite automaton from a given regular expression, and presents a dynamic programming solution that simulates copies of this automaton in seeking an alignment with maximum score containing the regular expression. We generalize this approach: 1) We introduce a variation of the problem for multiple sequences, namely the regular expression constrained multiple sequence alignment, and present an algorithm for it; 2) We develop an algorithm for the case of the problem when the alignments sought are required to contain a given sequence of regular expressions.","PeriodicalId":330810,"journal":{"name":"2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"35 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":"134305203","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: 23

Neuro-fuzzy Prediction of Biological Activity and Rule Extraction for HIV-1 Protease Inhibitors HIV-1蛋白酶抑制剂生物活性的神经模糊预测及规则提取

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594906

Razvan Andonie, L. Fabry-Asztalos, Catharine Collar, Sarah Abdul-Wahid, N. Salim

引用次数: 12

LVQ Approach Using AA Indices for Protein Subcellular Localisation Prediction 利用AA指数进行蛋白亚细胞定位预测的LVQ方法

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594932

Kok-Sin Toh, M. N. Nguyen, Jagath Rajapakse

引用次数: 1