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

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Utilizing Artificial Neural Networks to Elucidate Serum Biomarker Patterns Which Discriminate Between Clinical Stages in Melanoma 利用人工神经网络阐明区分黑色素瘤临床分期的血清生物标志物模式
2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594954
L. Lancashire, S. Ugurel, C. Creaser, D. Schadendorf, R. Rees, G. Ball
{"title":"Utilizing Artificial Neural Networks to Elucidate Serum Biomarker Patterns Which Discriminate Between Clinical Stages in Melanoma","authors":"L. Lancashire, S. Ugurel, C. Creaser, D. Schadendorf, R. Rees, G. Ball","doi":"10.1109/CIBCB.2005.1594954","DOIUrl":"https://doi.org/10.1109/CIBCB.2005.1594954","url":null,"abstract":"The identification of proteomic patterns from biomarkers in diseases such as cancer could lead to the determination of novel prognostic and diagnostic markers fundamental to the treatment of patients. We apply a recently developed approach utilizing artificial neural networks as a data mining tool to identify and characterize the best subset of biomarkers associated with melanoma. These were capable of predicting whether a sample is from a patient diagnosed with stage I or stage IV melanoma to median accuracies of 98 % on an independent subset of data used for validation. Furthermore, individual response curves have been generated allowing the investigation of whether these markers are up or down regulated with regards to tumor progression.","PeriodicalId":330810,"journal":{"name":"2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"21 2 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":"116780035","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: 5
Evaluating Robustness of Algorithm for Microsatellite Marker Genotyping 评价微卫星标记基因分型算法的稳健性
2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594912
Toshiko Matsumoto, R. Nakashige
{"title":"Evaluating Robustness of Algorithm for Microsatellite Marker Genotyping","authors":"Toshiko Matsumoto, R. Nakashige","doi":"10.1109/CIBCB.2005.1594912","DOIUrl":"https://doi.org/10.1109/CIBCB.2005.1594912","url":null,"abstract":"Microsatellites provide powerful genetic tools for complex disease mapping. Microsatellite genotyping requires analyzing peak data for discrimination of the true allele. In a previous study, we developed a new algorithm for automated genotyping. Here, we evaluate our algorithm’s robustness. First, we found that our algorithm calculates the model parameter of noise peaks appropriately and infers genotypes correctly even with low selectivity and specificity in the intermediate result of its first step. Our results indicate the model robustly calculates noise peaks. Second, our algorithm adequately infers true allele peaks for small sample sets. Furthermore, we evaluated its potential risk of failing to construct noise peak model.","PeriodicalId":330810,"journal":{"name":"2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"71 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":"127379141","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
ERP signal identification of Individuals at Risk for Alcoholism using Learning Vector Quantization Network 基于学习向量量化网络的酒精中毒危险个体ERP信号识别
2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594930
C. Lopes, Erik Schüler, P. Engel, A. Susin
{"title":"ERP signal identification of Individuals at Risk for Alcoholism using Learning Vector Quantization Network","authors":"C. Lopes, Erik Schüler, P. Engel, A. Susin","doi":"10.1109/CIBCB.2005.1594930","DOIUrl":"https://doi.org/10.1109/CIBCB.2005.1594930","url":null,"abstract":"In this work, a correlation between Event Related Potential (ERP) and visual memory, generally located in occipito-temporal region was found for two classes of subject: a sample with high risk (HR) for alcoholism and a sample of control subjects with low risk (LR). For the ERPs of matching stimulus we describe an application of an artificial neural network (ANN) algorithm proposed by Kohonen and namely Learning Vector Quantization (LVQ) for the classification of ERPs signals from individuals at HR and LR for alcoholism. After training, the LVQs nets were able to correctly classify about 80% of the HR and LR class of ERP. The results of this study suggest, as well, that the reduced amplitude of the c247 and P3 to matching stimuli appears to characterize subjects at HR for alcoholism.","PeriodicalId":330810,"journal":{"name":"2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"17 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":"123421135","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
Protein-Protein Interaction Prediction Based on Sequence Data by Support Vector Machine with Probability Assignment 基于序列数据的概率分配支持向量机蛋白质-蛋白质相互作用预测
2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594935
Jiankuan Ye, C. Kulikowski, I. Muchnik
{"title":"Protein-Protein Interaction Prediction Based on Sequence Data by Support Vector Machine with Probability Assignment","authors":"Jiankuan Ye, C. Kulikowski, I. Muchnik","doi":"10.1109/CIBCB.2005.1594935","DOIUrl":"https://doi.org/10.1109/CIBCB.2005.1594935","url":null,"abstract":"In this paper, we investigate the sequence-based protein-protein interaction prediction by machine learning methods. Specifically, we propose to build classifiers in the space of domain pairs, which are purely based on sequence data. We designed a novel way to select negative samples using a classification-based iterative voting procedure, and systematically compared the effects of negative sample selection on the performance of classification. We also propose an approach to estimate the probabilities for the predictions by SVM. Based on the selected negative samples, we compared nonlinear SVM based on gaussian kernel, linear SVM and linear logistic regression for both classification performance and probability assignments. Our results show that the probability assigned by SVM is more natural than logistic regression, and SVM also outperforms logistic regression for prediction.","PeriodicalId":330810,"journal":{"name":"2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"92 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":"131624506","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: 2
NMR Analysis of RNA Bulged Structures: Tabu Search Application in NOE Signal Assignment RNA凸起结构的核磁共振分析:禁忌搜索在NOE信号分配中的应用
2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594914
Marta Szachniuk, L. Popenda, Z. Gdaniec, R. Adamiak, J. Błażewicz
{"title":"NMR Analysis of RNA Bulged Structures: Tabu Search Application in NOE Signal Assignment","authors":"Marta Szachniuk, L. Popenda, Z. Gdaniec, R. Adamiak, J. Błażewicz","doi":"10.1109/CIBCB.2005.1594914","DOIUrl":"https://doi.org/10.1109/CIBCB.2005.1594914","url":null,"abstract":"Bulges belong to the most frequently occurring RNA secondary structural elements of high functional importance. Complete recognition of their spatial structure in solution requires application of Nuclear Magnetic Resonance (NMR) spectroscopy methods. A considerable part of NMR analytical process for RNA is performed automatically. However, in resonance assignment, being the process’ first computational step, manual assistance is still essential. We propose a tabu search algorithm being a tool for an automatic resonance assignment. The assignment is determined by NOE (Nuclear Overhauser Effect) pathways, which can be constructed in aromatic/anomeric region of 2D-NOESY (Nuclear Overhauser Effect SpectroscopY) spectrum generated during NMR experiment. Computational tests demonstrate performance of the tabu search applied to the experimental spectra of RNA bulged duplexes.","PeriodicalId":330810,"journal":{"name":"2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"16 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":"124607343","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
Diffuse Large B-cell Lymphoma Classification Using Genetic Programming Classifier 利用遗传规划分类器进行弥漫性大b细胞淋巴瘤分类
2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594937
S. Hengpraprohm, P. Chongstitvatana
{"title":"Diffuse Large B-cell Lymphoma Classification Using Genetic Programming Classifier","authors":"S. Hengpraprohm, P. Chongstitvatana","doi":"10.1109/CIBCB.2005.1594937","DOIUrl":"https://doi.org/10.1109/CIBCB.2005.1594937","url":null,"abstract":"Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin’s lymphoma. It is possible to classify normal and DLBCL patients using the data from cDNA microarrays technique that monitoring gene expression. Machine learning techniques are well-known methods for classification tasks. In this paper, we propose a Genetic Programming based method to generate classifiers with high accuracy. The proposed method employs cluster of classifiers to vote for the result. Furthermore, the classifier is presented in form of a mathematical equation which is amendable to human interpretation.","PeriodicalId":330810,"journal":{"name":"2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"25 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":"132961911","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
Modeling Transcriptional Regulation in Chondrogenesis Using Particle Swarm Optimization 利用粒子群优化技术模拟软骨形成过程中的转录调控
2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594934
Yunlong Liu, H. Yokota
{"title":"Modeling Transcriptional Regulation in Chondrogenesis Using Particle Swarm Optimization","authors":"Yunlong Liu, H. Yokota","doi":"10.1109/CIBCB.2005.1594934","DOIUrl":"https://doi.org/10.1109/CIBCB.2005.1594934","url":null,"abstract":"Chondrogenesis is a complex developmental process involving many transcription factors. Using mRNA expression data and regulatory DNA sequences, we formulated a quantitative model to predict a set of transcription-factor binding motifs (TFBMs) as a combinatorial problem. To solve such a problem, an efficient computational algorithm should be employed. In the current study, particle swarm optimization was applied. Swarm intelligence is an artificial intelligence approach that mimics a behavior of swarm-forming agents. Such systems are made up with a population of individuals that interact locally and globally. Here, a group of TFBMs was predicted using 200 artificial bees and the results were compared to biologically known binding motifs.","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":"133614784","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: 6
A Genetic Algorithm Approach for Discovering Diagnostic Patterns in Molecular Measurement Data 分子测量数据诊断模式的遗传算法研究
2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594945
D. Schaffer, A. Janevski, M. Simpson
{"title":"A Genetic Algorithm Approach for Discovering Diagnostic Patterns in Molecular Measurement Data","authors":"D. Schaffer, A. Janevski, M. Simpson","doi":"10.1109/CIBCB.2005.1594945","DOIUrl":"https://doi.org/10.1109/CIBCB.2005.1594945","url":null,"abstract":"The objective of this work is the development of an algorithm that, after training, will be able to discriminate between disease classes in molecular data. The system proposed uses a genetic algorithm (GA) to achieve this discrimination. We apply our method to three publicly available data sets. Two of the data sets are based on microarray data that allow the simultaneous measurement of the expression levels of genes under different disease states. The third data set is based on serum proteomic pattern diagnostics of ovarian cancer using high-resolution mass spectrometry to extract a set of biomarker classifiers. We show how our methodology finds an abundance of different feature models, automatically selecting a subset of discriminatory features, whose classification accuracy is comparable to other approaches considered. This raises questions about how to choose among the many competing models, while simultaneously estimating the prediction accuracy of the chosen models.","PeriodicalId":330810,"journal":{"name":"2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"99 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":"133575861","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: 28
GEMSCORE: A New Empirical Energy Function for Protein Folding GEMSCORE:蛋白质折叠的新经验能量函数
2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594933
Y. Chiu, Jenn-Kang Hwang, Jinn-Moon Yang
{"title":"GEMSCORE: A New Empirical Energy Function for Protein Folding","authors":"Y. Chiu, Jenn-Kang Hwang, Jinn-Moon Yang","doi":"10.1109/CIBCB.2005.1594933","DOIUrl":"https://doi.org/10.1109/CIBCB.2005.1594933","url":null,"abstract":"We have developed a new energy function, termed GEMSCORE, for the protein structure prediction, which is an emergent problem in the field of computational structural biology. The GEMSCORE combines knowledge-based and physics-based energy functions. Instead of hundreds and thousands parameters used in many physics-based energy functions, we optimized nine weights of energy terms in the GEMSCORE by using a generic evolutionary method. These nine energy terms are the electrostatic, the der Waals, the hydrogen-bonding potential, and six terms for solvation potentials. The GEMSCORE has been evaluated on six decoy sets, including 96 proteins with more 70,000 structures. The result indicates that our method is able to successfully identify 74 native proteins from these 96 proteins. Our GEMSCORE is fast and simple to discriminate between native and nonnative structures from thousands of protein structure candidates in these decoy sets. We believe that the GEMSCORE is robust and should be a useful energy function for the protein structure prediction.","PeriodicalId":330810,"journal":{"name":"2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"27 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":"125170660","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
Cooperative Rec-I-DCM3: A Population-Based Approach for Reconstructing Phylogenies 合作Rec-I-DCM3:基于种群的系统发育重建方法
2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594908
T. Williams, Marc L. Smith
{"title":"Cooperative Rec-I-DCM3: A Population-Based Approach for Reconstructing Phylogenies","authors":"T. Williams, Marc L. Smith","doi":"10.1109/CIBCB.2005.1594908","DOIUrl":"https://doi.org/10.1109/CIBCB.2005.1594908","url":null,"abstract":"In this paper, we study the use of cooperation as a technique for designing faster algorithms for reconstructing phylogenetic trees. Our focus is on the use of cooperation to reconstruct trees based on maximum parsimony. Our baseline algorithm is Rec-I-DCM3, the best-performing MP algorithm known-to-date. Our results demonstrate that cooperation does improve the performance of the baseline algorithm by at least an order of magnitude in terms of running time. The use of cooperation also established a new best known score on one of our datasets.","PeriodicalId":330810,"journal":{"name":"2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"13 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":"115467246","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: 5
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