2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology最新文献

Preliminary Results for GAMI: A Genetic Algorithms Approach to Motif Inference GAMI的初步结果:一种基序推理的遗传算法

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2005-12-01 DOI: 10.1109/CIBCB.2005.1594904

C. Congdon, Charles Fizer, N. W. Smith, H. Gaskins, Joseph C. Aman, G. Nava, C. Mattingly

引用次数: 38

An Internet-based Melanoma Diagnostic System - Toward the Practical Application - 基于互联网的黑色素瘤诊断系统——走向实际应用

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2005-12-01 DOI: 10.1109/CIBCB.2005.1594952

H. Iyatomi, H. Oka, M. Hashimoto, Masaru Tanaka, K. Ogawa

引用次数: 16

Multicategory Classification using Extended SVM-RFE and Markov Blanket on SELDI-TOF Mass Spectrometry Data 基于扩展SVM-RFE和Markov毯的SELDI-TOF质谱数据多类别分类

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2005-12-01 DOI: 10.1109/CIBCB.2005.1594938

J. Oh, Jean X. Gao, A. Nandi, Prem Gurnani, Lynne Knowles, J. Schorge, K. Rosenblatt

{"title":"Multicategory Classification using Extended SVM-RFE and Markov Blanket on SELDI-TOF Mass Spectrometry Data","authors":"J. Oh, Jean X. Gao, A. Nandi, Prem Gurnani, Lynne Knowles, J. Schorge, K. Rosenblatt","doi":"10.1109/CIBCB.2005.1594938","DOIUrl":"https://doi.org/10.1109/CIBCB.2005.1594938","url":null,"abstract":"Surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry data has been increasingly analyzed for identifying biomarkers for disease to help early detection of the disease. Recently, support vector machine (SVM) algorithm based on recursive feature elimination (RFE) was proposed to find a set of genes for cancer classification. In our study, we extend the SVM-RFE such that it can be used in the multicategory classification work using SELDI-TOF mass spectrometry data and propose a new feature selection algorithm (SVM-MB/RFE : SVM-Markov Blanket/Recursive Feature Elimination). In the preprocessing task of SVM-MB/RFE, ANOVA (Analysis of Variance) and binning methods are used for feature filtering. We demonstrate that the performance is improved through the preprocessing work. Compared with other methods such as not only SVM-RFE and Markov blanket but also PCA (Principle Components Analysis)+LDA (Linear Discriminant Analysis) and other feature selection algorithms, SVM-MB/RFE performs better than them.","PeriodicalId":330810,"journal":{"name":"2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124141208","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

Improving Protein Secondary-Structure Prediction by Predicting Ends of Secondary-Structure Segments 通过预测蛋白质二级结构片段的末端来提高蛋白质二级结构的预测

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2005-12-01 DOI: 10.1109/CIBCB.2005.1594959

U. Midic, Dunker Ak, Z. Obradovic

引用次数: 7

A Kernel Method for MicroRNA Target Prediction Using Sensible Data and Position-Based Features 基于敏感数据和位置特征的MicroRNA目标预测核方法

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2005-12-01 DOI: 10.1109/CIBCB.2005.1594897

Sung-Kyu Kim, Jin-Wu Nam, Wha-Jin Lee, Byoung-Tak Zhang

{"title":"A Kernel Method for MicroRNA Target Prediction Using Sensible Data and Position-Based Features","authors":"Sung-Kyu Kim, Jin-Wu Nam, Wha-Jin Lee, Byoung-Tak Zhang","doi":"10.1109/CIBCB.2005.1594897","DOIUrl":"https://doi.org/10.1109/CIBCB.2005.1594897","url":null,"abstract":"MicroRNAs (miRNAs) are small endogenous RNAs of ~ 22nt that act as direct post-transcriptional regulators in animals and plants. MicroRNAs generally perform a function by binding to the complementary site on the 3’ untranslated region of its target gene and especially the 8mers on the 5’ part of miRNA seems important as a seed. Computational methods for miRNA target prediction have been focusing on this seed region, but recent researches revealed that the specificity of the seed region may be sharply decreased even by a point mutation. In this paper, we present a kernel method for miRNA target prediction in animals, which improves the prediction performance with biologically sensible data and position-based features reflecting the way of miRNA: mRNA pairing mechanism. In building a training dataset, we choose experimentally verified data only to improve the quality of dataset by excluding randomly synthesized one and consequently to make the result of learning valid. We use sensitivity, specificity, and area under ROC curve as performance measures of our algorithm and compare the results of various dataset configurations. The overall results were 92.1% in sensitivity, 83.3% in specificity, and 0.931 in area under ROC curve. With position-based features, an increase of 3.3% in sensitivity and 1.6% in specificity were observed.","PeriodicalId":330810,"journal":{"name":"2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134211227","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: 18

Artificial Neural Network Analysis of DNA Microarray-based Prostate Cancer Recurrence 基于DNA微阵列的前列腺癌复发人工神经网络分析

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2005-12-01 DOI: 10.1109/CIBCB.2005.1594929

Leif E. Peterson, M. Ozen, Halime Erdem, Andrew Amini, L. Gomez, C. Nelson, M. Ittmann

引用次数: 24

Network Motifs, Feedback Loops and the Dynamics of Genetic Regulatory Networks 网络基序，反馈回路和遗传调控网络的动力学

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2005-12-01 DOI: 10.1109/CIBCB.2005.1594903

J. Hallinan, P. Jackway

引用次数: 23

Predicting Structural Disruption of Proteins Caused by Crossover 预测交叉引起的蛋白质结构破坏

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2005-11-01 DOI: 10.1109/CIBCB.2005.1594962

Denis C. Bauer, M. Bodén, R. Thier, Zheng Yuan

引用次数: 0

Truncated Profile Hidden Markov Models 截断轮廓隐马尔可夫模型

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594926

Scott F. Smith

引用次数: 1

System Identification and Nonlinear Factor Analysis for Discovery and Visualization of Dynamic Gene Regulatory Pathways 动态基因调控通路发现与可视化的系统辨识与非线性因子分析

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594901

A. Darvish, K. Najarian, D. Jeong, W. Ribarsky

引用次数: 6