2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology最新文献_第6页

A Novel Bioinformatics Technique For Predicting Condition-Specific Transcription Factor Binding Sites 预测条件特异性转录因子结合位点的新型生物信息学技术

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594918

V. Desai, P. Khatri, A. Done, Aviva Friedman, M. Tainsky, S. Drăghici

引用次数: 4

Two-Phase EA/k-NN for Feature Selection and Classification in Cancer Microarray Datasets 基于两阶段EA/k-NN的癌症微阵列数据特征选择与分类

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594891

Thorhildur Juliusdottir, D. Corne, E. Keedwell, A. Narayanan

{"title":"Two-Phase EA/k-NN for Feature Selection and Classification in Cancer Microarray Datasets","authors":"Thorhildur Juliusdottir, D. Corne, E. Keedwell, A. Narayanan","doi":"10.1109/CIBCB.2005.1594891","DOIUrl":"https://doi.org/10.1109/CIBCB.2005.1594891","url":null,"abstract":"Efficient and reliable methods that can find a small sample of informative genes amongst thousands are of great importance. In this area, much research is investigating the combination of advanced search strategies (to find subsets of features), and classification methods. We investigate a simple evolutionary algorithm/classifier combination on two microarray cancer datasets, where this combination is applied twice – once for feature selection, and once for further selection and classification. Our contribution are: (further) demonstration that a simple EA/classifier combination is capable of good feature discovery and classification performance with no initial dimensionality reduction; demonstration that a simple repeated EA/k-NN approach is capable of competitive or better performance than methods using more sophisticated preprocessing and classifer methods; new and challenging results on two public datasets with clear explanation of experimental setup; review material on the EA/kNN area; and specific identification of genes that our work suggests are significant regarding colon cancer and prostate cancer.","PeriodicalId":330810,"journal":{"name":"2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"3 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":"115815733","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: 27

Feature Selection for Microarray Data Using Least Squares SVM and Particle Swarm Optimization 基于最小二乘支持向量机和粒子群优化的微阵列数据特征选择

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594892

E. Tang, P. N. Suganthan, X. Yao

引用次数: 43

Improving Protein Function Prediction using the Hierarchical Structure of the Gene Ontology 利用基因本体的层次结构改进蛋白质功能预测

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594940

Roman Eisner, B. Poulin, D. Szafron, P. Lu, R. Greiner

{"title":"Improving Protein Function Prediction using the Hierarchical Structure of the Gene Ontology","authors":"Roman Eisner, B. Poulin, D. Szafron, P. Lu, R. Greiner","doi":"10.1109/CIBCB.2005.1594940","DOIUrl":"https://doi.org/10.1109/CIBCB.2005.1594940","url":null,"abstract":"High performance and accurate protein function prediction is an important problem in molecular biology. Many contemporary ontologies, such as Gene Ontology (GO), have a hierarchical structure that can be exploited to improve the prediction accuracy, and lower the computational cost, of protein function prediction. We leverage the hierarchical structure of the ontology in two ways. First, we present a method of creating hierarchy-aware training sets for machine-learned classifiers and we show that, in the case of GO molecular function, it is the most accurate method compared to not considering the hierarchy during training. Second, we use the hierarchy to reduce the computational cost of classification. We also introduce a sound methodology for evaluating hierarchical classifiers using global cross-validation. Biologists often use sequence similarity (e.g. BLAST) to identify a \" nearest neighbor\" sequence and use the database annotations of this neighbor to predict protein function. In these cases, we use the hierarchy to improve accuracy by a small amount. When no similar sequences can be found (which is true for up to 40% of some common proteomes), our technique can improve accuracy by a more significant amount. Although this paper focuses on a specific important application-protein function prediction for the GO hierarchy-the techniques may be applied to any classification problem over a hierarchical ontology.","PeriodicalId":330810,"journal":{"name":"2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"6 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":"129812511","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: 103

Extracting Features from Protein Sequences Using Chinese Segmentation Techniques for Subcellular Localization 基于中文分割技术的蛋白质序列特征提取及其亚细胞定位

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594931

Yang Yang, Bao-Liang Lu

引用次数: 12

Neural Spike Communication under Noisy Environments 噪声环境下的神经脉冲通信

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594913

N. Homma, K. Fuchigami, M. Gupta

引用次数: 0

Robust Algorithm for Finding Weak Motifs 寻找弱基序的鲁棒算法

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594948

X. Yang, Jagath Rajapakse

引用次数: 0

A Novel Variation Operator for More Rapid Evolution of DNA Error Correcting Codes. 一种用于DNA纠错码快速进化的变异算子。

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594898

D. Ashlock, S. Houghten

引用次数: 10

An Efficient Sequential RBF Network for Gene Expression-Based Multi-category classification 基于基因表达的高效序列RBF网络多类别分类

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594925

Runxuan Zhang, N. Sundararajan, G. Huang, P. Saratchandran

引用次数: 3

Segment and Combine Approach for Biological Sequence Classification 生物序列分段与组合分类方法

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594917

P. Geurts, Antia Blanco Cuesta, L. Wehenkel

引用次数: 7