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

Designing artificial organisms for use in biological simulations 设计用于生物模拟的人工有机体

2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2011-04-11 DOI: 10.1109/CIBCB.2011.5948463

W. Ashlock, D. Ashlock

{"title":"Designing artificial organisms for use in biological simulations","authors":"W. Ashlock, D. Ashlock","doi":"10.1109/CIBCB.2011.5948463","DOIUrl":"https://doi.org/10.1109/CIBCB.2011.5948463","url":null,"abstract":"In this paper we investigate two types of artificial organism which have the potential to be useful in biological simulations at the genomic level, such as simulations of speciation or gene interaction. Biological problems of this type are usually studied either with simulations using artificial genes that are merely evolving strings with no phenotype, ignoring the possibly crucial contribution of natural selection, or with real biological data involving so much complexity that it is difficult to sort out the important factors. This research provides a middle ground. The artificial organisms are: gridwalkers (GWs), a variation on the self-avoiding walk problem, and plus-one-recall-store (PORS), a simple genetic programming maximum problem implemented with a context free grammar. Both are known to have rugged multimodal fitness landscapes. We define a new variation operator, a kind of aligned crossover for variable length strings, which we call Smith-Waterman crossover. The problems, using Smith-Waterman crossover, size-neutral crossover (a kind of non-aligned crossover defined in [3]), mutation only, and horizontal gene transfer (such as occurs in biology with retroviruses) are explored. We define a measure called fitness preservation to quantify the differences in their fitness landscapes and to provide guidance to researchers in determining which problem/variation operator set is best for their simulation.","PeriodicalId":395505,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"8 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":"127300972","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

Predicting coding region candidates in the DNA sequence based on visualization without training 基于可视化的非训练DNA序列候选编码区预测

2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2011-04-11 DOI: 10.1109/CIBCB.2011.5948454

Bo Chen, P. Ji

{"title":"Predicting coding region candidates in the DNA sequence based on visualization without training","authors":"Bo Chen, P. Ji","doi":"10.1109/CIBCB.2011.5948454","DOIUrl":"https://doi.org/10.1109/CIBCB.2011.5948454","url":null,"abstract":"Identifying the protein coding regions in the DNA sequence is an active issue in computational biology. Presently, there are many outstanding methods in predicting the coding regions with extreme high accuracy, after conducting preceding training process. However, the training dependence may reduce adaptability of the methods, particularly for new sequences from unknown organisms with no or small training sets. In this paper, we firstly present a Self Adaptive Spectral Rotation (SASR) approach, which was first introduced in a previous work published in Nucleic Acids Research. This approach is adopted to visualize the Triplet Periodicity (TP) property, which is a simple and universal coding related property. After that, we use a segmentation technique to computationally analyze the visualization and provide a numerical prediction of the coding region candidates in the DNA sequence. This approach does not require any training process, so it can work before any extra information is available, especially is helpful when dealing with new sequences from unknown organisms. Hence, it could be an efficient tool for coding region prediction in the early stage study.","PeriodicalId":395505,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"6 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":"124338439","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

Regularized linear discriminant analysis and its recursive implementation for gene subset selection 基因子集选择的正则化线性判别分析及其递归实现

2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2011-04-11 DOI: 10.1109/CIBCB.2011.5948468

K. Mao, Feng Yang, W. Tang

引用次数: 2

Accelerating kernel clustering for biomedical data analysis 生物医学数据分析的加速核聚类

2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2011-04-11 DOI: 10.1109/CIBCB.2011.5948460

A. Gisbrecht, B. Hammer, Frank-Michael Schleif, Xibin Zhu

引用次数: 3

Organizational texts classification using artificial immune recognition systems 利用人工免疫识别系统进行组织文本分类

2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2011-04-11 DOI: 10.1109/CIBCB.2011.5948456

N. Forouzideh, M. Mahmoudi, K. Badie

引用次数: 8

An incremental method for mosaicing of optical microscope imagery 光学显微镜图像拼接的增量方法

2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2011-04-11 DOI: 10.1109/CIBCB.2011.5948458

L. Carozza, A. Bevilacqua, F. Piccinini

{"title":"An incremental method for mosaicing of optical microscope imagery","authors":"L. Carozza, A. Bevilacqua, F. Piccinini","doi":"10.1109/CIBCB.2011.5948458","DOIUrl":"https://doi.org/10.1109/CIBCB.2011.5948458","url":null,"abstract":"Digital imaging is nowadays widely employed in the field of optical microscopy. One of the most apparent benefits consists in the possibility for the researcher to see the whole biological sample in one image, achieved by collecting all the parts being inspected. Common approaches work in batch mode and rely on known motorized x–y stage offsets of the microscope holder. Or alternatively, the methods are conceived just to provide visually pleasant mosaics off-line, that are often built by altering the photometric values or the geometric properties of the original component images. This work presents an incremental mosaicing method for optical microscopy imagery, compliant with on-line requirements and suitable even for non-motorized microscopes. The resulting mosaics are very accurate and preserve the consistency of the original images so to be used for further global measurement steps. Nevertheless, the mosaics are visually pleasant so to be used for visual inspection as well. The experimental results obtained in different biological examinations confirm the efficacy of our approach.","PeriodicalId":395505,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"2 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":"126332340","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: 14

A simulation of bacterial communities 细菌群落的模拟

2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2011-04-11 DOI: 10.1109/CIBCB.2011.5948465

D. Ashlock, Andrew McEachern

{"title":"A simulation of bacterial communities","authors":"D. Ashlock, Andrew McEachern","doi":"10.1109/CIBCB.2011.5948465","DOIUrl":"https://doi.org/10.1109/CIBCB.2011.5948465","url":null,"abstract":"This study constructs and tests an agent-based model of bacterial communities with the goal of modeling the observation that the majority of bacteria in nature cannot be cultured. The new field of metagenomics, the direct, mass sequencing of DNA recovered from the environment, is the source of this observation. The hypothesis tested is that bacteria form interdependent communities so that viable levels of energy production are rare in bacteria when they are grown in monoculture. A new game, the metabolism game is introduced. Agents produce energy by playing this game with one another. Studies are run with different number of bacterial species in the simulation. The energy level for viability is set by running simulations with a single bacterial species and then the hypothesis is tested in simulations with multiple bacterial species. Multiple bacterial species are evolved in a novel type of multi-population evolutionary algorithm called a multiple worlds algorithm. The fraction of culturable bacterial agents recovered from the simulation is larger than that found in nature but still quite low, supporting the hypothesis that bacteria may not be culturable because they require the presence of partner species.","PeriodicalId":395505,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"33 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":"126351326","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

Combined covariance model for non-coding RNA gene finding 非编码RNA基因发现的联合协方差模型

2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2011-04-11 DOI: 10.1109/CIBCB.2011.5948474

Wenbo Jiang, K. Wiese

引用次数: 5

Comparative analysis of machine learning techniques for the prediction of logP logP预测的机器学习技术比较分析

2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2011-04-11 DOI: 10.1109/CIBCB.2011.5948478

Edward W. Lowe, Mariusz Butkiewicz, Matthew Spellings, A. Omlor, J. Meiler

{"title":"Comparative analysis of machine learning techniques for the prediction of logP","authors":"Edward W. Lowe, Mariusz Butkiewicz, Matthew Spellings, A. Omlor, J. Meiler","doi":"10.1109/CIBCB.2011.5948478","DOIUrl":"https://doi.org/10.1109/CIBCB.2011.5948478","url":null,"abstract":"Several machine learning techniques were evaluated for the prediction of logP. The algorithms used include artificial neural networks (ANN), support vector machines (SVM) with the extension for regression, and kappa nearest neighbor (k-NN). Molecules were described using optimized feature sets derived from a series of scalar, two- and three-dimensional descriptors including 2-D and 3-D autocorrelation, and radial distribution function. Feature optimization was performed as a sequential forward feature selection. The data set contained over 25,000 molecules with experimentally determined logP values collected from the Reaxys and MDDR databases, as well as data mining through SciFinder. LogP, the logarithm of the equilibrium octanol-water partition coefficient for a given substance is a metric of the hydrophobicity. This property is an important metric for drug absorption, distribution, metabolism, and excretion (ADME). In this work, models were built by systematically optimizing feature sets and algorithmic parameters that predict logP with a root mean square deviation (rmsd) of 0.86 for compounds in an independent test set. This result presents a substantial improvement over XlogP, an incremental system that achieves a rmsd of 1.41 over the same dataset. The final models were 5-fold cross-validated. These fully in silico models can be useful in guiding early stages of drug discovery, such as virtual library screening and analogue prioritization prior to synthesis and biological testing. These models are freely available for academic use.","PeriodicalId":395505,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"23 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":"133106427","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

Derivation of minimum best sample size from microarray data sets: A Monte Carlo approach 从微阵列数据集推导最小最佳样本量:蒙特卡罗方法

2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2011-04-11 DOI: 10.1109/CIBCB.2011.5948461

Chengpeng Bi, M. Becker, J. Leeder

引用次数: 2