2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)最新文献

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Fitting contact networks to epidemic behavior with an evolutionary algorithm 用进化算法拟合传染病行为的接触网络
D. Ashlock, E. Shiller
{"title":"Fitting contact networks to epidemic behavior with an evolutionary algorithm","authors":"D. Ashlock, E. Shiller","doi":"10.1109/CIBCB.2011.5948466","DOIUrl":"https://doi.org/10.1109/CIBCB.2011.5948466","url":null,"abstract":"Epidemic models often incorporate contact networks along which the disease can be passed. This study incorporates a restarting-recentering evolutionary algorithm, previously developed to locate extremal epidemic networks, together with a new representation, the toggle-delete representation, for evolvable networks. The goal is to locate networks that were likely to have produced a given epidemic behavior. This goal subsumes a new fitness function for driving selection in network evolution. Earlier representations used networks with a fixed sequence of contact numbers. The new representation can add and remove edges from the network, permitting a search that varies contact numbers within the network. A parameter setting study is performed on an epidemic profile obtained from an random network and then tested on a bimodal profile invented by the researchers. The algorithm succeeds in producing networks that cause epidemics run on them to mimic the specified epidemic profiles.","PeriodicalId":395505,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116802035","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: 21
Discovery of MicroRNA markers: An SVM-based multiobjective feature selection approach MicroRNA标记物的发现:基于svm的多目标特征选择方法
A. Mukhopadhyay, U. Maulik, S. Bandyopadhyay
{"title":"Discovery of MicroRNA markers: An SVM-based multiobjective feature selection approach","authors":"A. Mukhopadhyay, U. Maulik, S. Bandyopadhyay","doi":"10.1109/CIBCB.2011.5948473","DOIUrl":"https://doi.org/10.1109/CIBCB.2011.5948473","url":null,"abstract":"MicroRNAs (miRNAs) are small non-coding RNAs that have been shown to play important roles in gene regulation and various biological processes. The abnormal expression of some specific miRNAs often results in the development of cancer. In this article, we have utilized a multiobjective genetic algorithm-based feature selection algorithm wrapped with support vector machine (SVM) classifier for selecting promising miRNAs having differential expression in benign and malignant tissue samples. Subsequently, the non-dominated sets of promising miRNAs are aggregated into a single most promising miRNA subset. Finally, the Signal-to-Noise Ratio (SNR) statistic has been applied on the obtained miRNA subset for identifying potential miRNA markers that distinguish the two classes (benign and malignant) of tissue samples. The performance has been demonstrated on four real-life miRNA expression datasets for different SVM kernel functions and the identified miRNA markers are reported.","PeriodicalId":395505,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128295974","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
Isolating - a new resampling method for gene order data 分离——一种新的基因序列重采样方法
Jian Shi, W. Arndt, Fei Hu, Jijun Tang
{"title":"Isolating - a new resampling method for gene order data","authors":"Jian Shi, W. Arndt, Fei Hu, Jijun Tang","doi":"10.1109/CIBCB.2011.5948464","DOIUrl":"https://doi.org/10.1109/CIBCB.2011.5948464","url":null,"abstract":"The purpose of using resampling methods on phylogenetic data is to estimate the confidence value of branches. In recent years, bootstrapping and jackknifing are the two most popular resampling schemes which are widely used in biological reserach. However, for gene order data, traditional bootstrap procedures can not be applied because gene order data is viewed as one character with various states. Experience in the biological community has shown that jackknifing is a useful means of determining the confidence value of a gene order phylogeny. When genomes are distant, however, applying jackknifing tends to give low confidence values to many valid branches, causing them to be mistakenly removed. In this paper, we propose a new method that overcomes this disadvantage of jackknifing and achieves better accuracy and confidence values for gene order data. Compared to jackknifing, our experimental results show that the proposed method can produce phylogenies with lower error rates and much stronger support for good branches. We also establish a theoretic lower bound regarding how many genes should be isolated, which is confirmed empirically.","PeriodicalId":395505,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127469901","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
Maximum likelihood phylogenetic reconstruction using gene order encodings 利用基因顺序编码的最大似然系统发育重建
Fei Hu, Nan Gao, Meng Zhang, Jijun Tang
{"title":"Maximum likelihood phylogenetic reconstruction using gene order encodings","authors":"Fei Hu, Nan Gao, Meng Zhang, Jijun Tang","doi":"10.1109/cibcb.2011.5948459","DOIUrl":"https://doi.org/10.1109/cibcb.2011.5948459","url":null,"abstract":"","PeriodicalId":395505,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124859065","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
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