2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology最新文献_第7页

Predicting Peroxisomal Proteins 预测过氧化物酶体蛋白

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594956

J. Hawkins, M. Bodén

引用次数: 5

Novel Algorithm for MALDI-TOF Baseline Drift Removal MALDI-TOF基线漂移去除新算法

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594946

J. Kolibal, Daniel Howard

引用次数: 4

Pathway Optimization of Biological Drug Response Networks 生物药物反应网络的途径优化

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594902

Chien-Feng Huang, C. Forst

{"title":"Pathway Optimization of Biological Drug Response Networks","authors":"Chien-Feng Huang, C. Forst","doi":"10.1109/CIBCB.2005.1594902","DOIUrl":"https://doi.org/10.1109/CIBCB.2005.1594902","url":null,"abstract":"One of the major challenges in systems biology today is to devise generally robust methods of interpreting data concerning the expression levels of the genes in an organism in a way that will shed light on the complex relationships between multiple genes and their products. The ability to better understand and predict the structures and actions of complex biological systems is of significant importance to modern drug discovery as well as our understanding of the mechanisms behind an organism’s ability to react to its environment. In this paper we present a study for robust biological pathway construction through genetic algorithms. The platform is based on the construction of biological networks given different sets of interaction information and the optimization of sub-networks constrained by the gene expression data. As an application, expression data of drug response in M. tuberculosis is used to build generic response subnetworks. Subnetworks are then compared to identify the essential key components that are common to different networks. We are thus able to identify essential nodes in specific drug response. We expect that this approach will provide robust prediction of response networks and accelerate target identification for drug development in the future.","PeriodicalId":330810,"journal":{"name":"2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"45 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":"126341162","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

A Transcriptional Approach to Gene Clustering 基因聚类的转录方法

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594921

I. Tagkopoulos

{"title":"A Transcriptional Approach to Gene Clustering","authors":"I. Tagkopoulos","doi":"10.1109/CIBCB.2005.1594921","DOIUrl":"https://doi.org/10.1109/CIBCB.2005.1594921","url":null,"abstract":"We present an integrative method for clustering coregulated genes and elucidating their underlying regulatory mechanisms. We use multi-state partition functions and thermodynamic models to derive six distinct correlation classes that correspond to various Protein-Protein and Protein-DNA interactions. We then introduce a biclustering algorithm for clustering genes based on the correlations exhibited in their expression profiles. We evaluate the functional enrichment and statistical significance of the resulting clusters using precision-recall curves. Our results show that classification performance can be optimized by selecting the corresponding correlation class. Additionally, there is a significant improvement over single class biclustering when we use multi-class support vector machines and biclustering scores as features. Furthermore, the analysis of the upstream regions of all genes comprising each cluster shows that the derived correlation classes capture the expression of genes with shared regulation. We identify over a hundred highly conserved sequences, among which twenty one match well-known regulatory motifs. Further analysis of the identified conserved sequences provides not only an explanation of the classification performance, but serves also as an indicator of the regulatory correlation for various groups.","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":"132885644","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

Training Reformulated Radial Basis Function Neural Networks Capable of Identifying Uncertainty in the Recognition of Videotaped Neonatal Seizures 训练可识别新生儿癫痫录像识别不确定性的重构径向基函数神经网络

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594953

N. Karayiannis, Yaohua Xiong

引用次数: 4

Gene Classification using Codon Usage and SVMs 基于密码子和支持向量机的基因分类

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594951

J. Ma, M. N. Nguyen, G.W.L. Pang, Jagath Rajapakse

{"title":"Gene Classification using Codon Usage and SVMs","authors":"J. Ma, M. N. Nguyen, G.W.L. Pang, Jagath Rajapakse","doi":"10.1109/CIBCB.2005.1594951","DOIUrl":"https://doi.org/10.1109/CIBCB.2005.1594951","url":null,"abstract":"A novel approach for gene classification is proposed, which adopts codon usage bias pattern as feature vector for the subsequent classification using Support Vector Machines (SVMs). A given DNA sequence is first converted to 59-dimensional feature vector, each element corresponding to the relative synonymous usage frequency of a codon. Therefore, the input to the classifier is independent of the size of the DNA sequences. Therefore, our approach is useful when the genes to be classified are of different length, where the homology-based methods are inapplicable due to the difficulty in the alignment of sequences having different lengths. The applicability and usage of the present method is demonstrated by a classification of 1841 HLA (Human Leukocyte Antigen) coding sequences selected from the database of IMGT/HLA. Using the codon usage frequencies, the binary SVM achieved accuracy up to 99.30% for classification human MHC (Major Histocompatibility Complex) molecules in their major classes: MHC-I and MHC-II. By using a multi-class SVM approach, the accuracy rates of 99.73% and 98.38% were achieved for subclasss classification of MHC-I and MHC-II classes, respectively. The results show that the proposed method is capable of accurately classifying MHC molecules in to their major classes as well as in to the subclasses within major classes. Also, the results of gene classification according to the codon usage bias pattern are consistent with the molecule structures and biological functions, further validating our approach.","PeriodicalId":330810,"journal":{"name":"2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"52 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":"126367731","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: 8

Utilizing Domain Knowledge in Neural Network Models for Peptide-Allele Binding Prediction 利用神经网络模型中的领域知识进行多肽-等位基因结合预测

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594941

V. Megalooikonomou, D. Kontos, N. DeClaris, P. Cano

引用次数: 2

Functional Distances for Genes Based on GO Feature Maps and their Application to Clustering 基于GO特征映射的基因功能距离及其聚类应用

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594910

N. Speer, H. Fröhlich, C. Spieth, A. Zell

引用次数: 5

A new distance measure of RNA ensembles and its application to phylogenetic tree construction 一种新的RNA集合距离测量方法及其在系统发育树构建中的应用

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594911

Sven Siebert, R. Backofen

{"title":"A new distance measure of RNA ensembles and its application to phylogenetic tree construction","authors":"Sven Siebert, R. Backofen","doi":"10.1109/CIBCB.2005.1594911","DOIUrl":"https://doi.org/10.1109/CIBCB.2005.1594911","url":null,"abstract":"A major challenge in RNA structure analysis is to infer common catalytic or regulatory functions based on primary sequences and secondary structures. Some programs have been developed that compare RNAs with such given structures. Nevertheless, the most important problem is that it is hard to determine the adopted structures of RNAs which are a necessary prerequisite to numerous applications; once a structure has been assigned to a sequence (e.g. the minimum free energy structure), it influences the output of the programs and thus affects the scientific result, especially when dealing with a set of multiple RNAs. In this paper, we go one step further and analyze distances between RNA structure ensembles. They reflect structural relationships computed basically on base-pairing probability matrices. We propose a distance measure between two base-pairing probability matrices showing similar or non-similar structural folding behaviour. This includes the detection of shared optimal, suboptimal and local secondary structures. Consequently, our distance measure avoids falling into the trap of fixing specific structures. A pairwise comparison strategy in a set of multiple RNAs leads us to construct a network of structural relationships using the neighbour joining method. Attempts to predict phylogenetic trees are discussed and demonstrated by means of viral RNAs.","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":"134171959","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

A Machine Learning Approach to Resolving Incongruence in Molecular Phylogenies and Visualization Analysis 解决分子系统发育不一致的机器学习方法及可视化分析

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594939

Xiaoxu Han

引用次数: 2