2013 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)最新文献

A hybrid colony fuzzy system for analyzing diabetes microarray data 用于分析糖尿病微阵列数据的混合菌落模糊系统

2013 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2013-04-16 DOI: 10.1109/CIBCB.2013.6595395

P. Ganeshkumar, S. Vijay, D. Devaraj

{"title":"A hybrid colony fuzzy system for analyzing diabetes microarray data","authors":"P. Ganeshkumar, S. Vijay, D. Devaraj","doi":"10.1109/CIBCB.2013.6595395","DOIUrl":"https://doi.org/10.1109/CIBCB.2013.6595395","url":null,"abstract":"Treatment to diabetes using microarray data has gained much attention among the physician as it provides important information about pathological states as well as information that can lead to earlier diagnosis. But its high dimensional low sample nature poses a lot of difficulties when it is analyzed by hand and needs an automatic system. As against statistical and machine learning approaches, fuzzy expert system provides an understandable diagnostic system. An important issue in the design of fuzzy expert system is knowledge acquisition. This paper presents a hybrid colony algorithm to extract if-then rules and to form membership functions from diabetes microarray data. During the run, Ant Colony Optimization (ACO) is used to generate optimal rule set and Artificial Bee Colony (ABC) is used to evolve the points of membership function. Mutual Information is used for identification of informative genes. The performance of the proposed approach is evaluated using two diabetes microarray data sets. From the simulation study, it is found that the proposed approach generated an accurate fuzzy system with interpretable rules when compared with other approaches.","PeriodicalId":350407,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129477076","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

Accelerating protein tertiary structure analysis based on FPGA 基于FPGA的蛋白质三级结构加速分析

2013 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2013-04-16 DOI: 10.1109/CIBCB.2013.6595416

Quanhua Shen, Xia Fei, Qianghua Zhu

引用次数: 0

Segmentation of mitochondria in intracellular space 细胞内线粒体的分割

2013 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2013-04-16 DOI: 10.1109/CIBCB.2013.6595412

Nhan Nguyen-Thanh, T. Pham, K. Ichikawa

引用次数: 4

Genetic algorithm for dimer-led and error-restricted spaced motif discovery 二聚体引导和错误限制间隔基序发现的遗传算法

2013 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2013-04-16 DOI: 10.1109/CIBCB.2013.6595409

Tak-Ming Chan, Leung-Yau Lo, M. Wong, Yong Liang, K. Leung

{"title":"Genetic algorithm for dimer-led and error-restricted spaced motif discovery","authors":"Tak-Ming Chan, Leung-Yau Lo, M. Wong, Yong Liang, K. Leung","doi":"10.1109/CIBCB.2013.6595409","DOIUrl":"https://doi.org/10.1109/CIBCB.2013.6595409","url":null,"abstract":"DNA motif discovery is an important problem for deciphering protein-DNA bindings in gene regulation. To discover generic spaced motifs which have multiple conserved patterns separated by wild-cards called spacers, the genetic algorithm (GA) based GASMEN has been proposed and shown to outperform related methods. However, the over-generic modeling of any number of spacers increases the optimization difficulty in practice. In protein-DNA binding case studies, complicated spaced motifs are rare while dimers with single spacers are more common spaced motifs. Moreover, errors (mismatches) in a conserved pattern are not arbitrarily distributed as certain highly conserved nucleotides are essential to maintain bindings. Motivated by better optimization in real applications, we have developed a new method, which is GA for Dimer-led and Error-restricted Spaced Motifs (GADESM). Common spaced motifs are paid special attention to using dimer-led initialization in the population initialization. The results on real datasets show that the dimer-led initialization in GADESM achieves better fitness than GASMEN with statistical significance. With additional error-restricted motif occurrence retrieval, GADESM has shown better performance than GASMEN on both comprehensive simulation data and a real ChIP-seq case study.","PeriodicalId":350407,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131357508","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

Designing predictors of halophilic and non-halophilic proteins using support vector machines 利用支持向量机设计嗜盐和非嗜盐蛋白的预测因子

2013 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2013-04-16 DOI: 10.1109/CIBCB.2013.6595414

Hui-Ling Huang, Y. S. Srinivasulu, Phasit Charoenkwan, Hua-Chin Lee, Shinn-Ying Ho

{"title":"Designing predictors of halophilic and non-halophilic proteins using support vector machines","authors":"Hui-Ling Huang, Y. S. Srinivasulu, Phasit Charoenkwan, Hua-Chin Lee, Shinn-Ying Ho","doi":"10.1109/CIBCB.2013.6595414","DOIUrl":"https://doi.org/10.1109/CIBCB.2013.6595414","url":null,"abstract":"Finding the molecular features causes the halophilicity in the halostable organisms is helpful to understand the halophilic adaption. In this study, we proposed a prediction method for halophilic proteins by using a machine learning method. The stages of this study are six-fold. First, we establish a non-redundant dataset of the halophilic proteins, collected from NCBI, Uniprotkb and EMBL-EBI databases. The dataset consists of 245 positive and negative proteins with sequence identity <;25%. Second, the protein sequences are represented by three types of feature vector sets which include amino acid composition, dipeptide composition, and physicochemical properties. Third, we propose three classifiers based on support vector machine (SVM) to classify the halophilic proteins and non-halophilic proteins. Fourth, the independent test accuracies of the three efficient classifiers are larger than 83%. Fifth, an inheritable biobjective combinatory genetic algorithm is utilized to select a set of 11 physicochemical properties (PCPs). Sixth, these abundant amino acids, high different dipeptides (amino acid pair) and 11 informative PCPs can support to analyze the halophilic and non-halophilic proteins.","PeriodicalId":350407,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128785023","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

The max-min high-order dynamic Bayesian network learning for identifying gene regulatory networks from time-series microarray data 从时序微阵列数据中识别基因调控网络的最大-最小高阶动态贝叶斯网络学习

2013 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2013-04-16 DOI: 10.1109/CIBCB.2013.6595392

Yifeng Li, A. Ngom

{"title":"The max-min high-order dynamic Bayesian network learning for identifying gene regulatory networks from time-series microarray data","authors":"Yifeng Li, A. Ngom","doi":"10.1109/CIBCB.2013.6595392","DOIUrl":"https://doi.org/10.1109/CIBCB.2013.6595392","url":null,"abstract":"We propose a new high-order dynamic Bayesian network (HO-DBN) learning approach, called Max-Min High-Order DBN (MMHO-DBN), for discrete time-series data. MMHO-DBN explicitly models the time lags between parents and target in an efficient manner. It extends the Max-Min Hill-Climbing Bayesian network (MMHC-BN) technique which was originally devised for learning a BN's structure from static data. Both Max-Min approaches are hybrid local learning methods which fuse concepts from both constraint-based Bayesian techniques and search-and-score Bayesian methods. The MMHO-DBN first uses constraint-based ideas to limit the space of potential structure and then applies search-and-score ideas to search for an optimal HO-DBN structure. We evaluated the ability of our MMHO-DBN approach to identify genetic regulatory networks (GRN's) from gene expression time-series data. Preliminary results on artificial and real gene expression time-series are encouraging and show that it is able to learn (long) time-delayed relationships between genes, and faster than current HO-DBN learning methods.","PeriodicalId":350407,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121715979","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: 16

Prediction of anti-EGFR drug resistance base on binding free energy and hydrogen bond analysis 基于结合自由能和氢键分析的抗egfr耐药性预测

2013 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2013-04-16 DOI: 10.1109/CIBCB.2013.6595408

Weiqiang Zhou, D. D. Wang, Hong Yan, M. Wong, V. Lee

引用次数: 7

Analysis of MRI-based cortical surface structure complexity in dementia by sample entropy 用样本熵分析基于mri的痴呆脑皮层表面结构复杂性

2013 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2013-04-16 DOI: 10.1109/CIBCB.2013.6595407

Ying Chen, T. Pham

{"title":"Analysis of MRI-based cortical surface structure complexity in dementia by sample entropy","authors":"Ying Chen, T. Pham","doi":"10.1109/CIBCB.2013.6595407","DOIUrl":"https://doi.org/10.1109/CIBCB.2013.6595407","url":null,"abstract":"Dementia is a most common neurodegenerative disorder. Previous researches have attempted to relate the impairment of cognition to volumes or thickness changes of the cortical regions, with relatively few studies investigating other features such as cortical surface anatomy. In the present study, we report for the first time to use sample entropy (SampEn) to assess the complexity of cortical surface structure in early stage of dementia compared to healthy controls. Whole brain structural T1-weighted MRI scans were collected from 192 subjects including patients with very mild and mild dementia (age = 77 ± 7, male/female = 41/55, CDR = 0.5 or 1, n = 96), and healthy subjects (aged = 76 ± 9, male/female= 25/70, CDR = 0, n = 96). SampEn was applied to each transection and averaged. Comparisons were made between control and dementia for the whole brain as well as for each sub-section of all layers. Results show an overall larger SampEn in demented group compared with non-demented group(p <; 0.05) which indicate an increase of structural irregularity of cortical surface in dementia. Our findings offer a novel approach for studying cortical atrophy patterns and can potentially be used to develop novel biomarkers of dementia.","PeriodicalId":350407,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128132086","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

A new hybrid probability-based method for identifying proteins and protein modifications 一种新的基于混合概率的蛋白质和蛋白质修饰识别方法

2013 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2013-04-16 DOI: 10.1109/CIBCB.2013.6595381

Penghao Wang, Susan R. Wilson

{"title":"A new hybrid probability-based method for identifying proteins and protein modifications","authors":"Penghao Wang, Susan R. Wilson","doi":"10.1109/CIBCB.2013.6595381","DOIUrl":"https://doi.org/10.1109/CIBCB.2013.6595381","url":null,"abstract":"Tandem mass spectrometry is a powerful tool for studying proteins and protein post-translational modifications. However, typically less than half of the proteins in a complex sample can be successfully identified. The low identification coverage is largely due to the presence of various protein modifications, which usually lead to incorrect protein identifications by existing methods. Therefore, how to effectively detect protein modifications simultaneously with protein identification is crucial for improving the identification coverage and accuracy. We have developed a new hybrid probability-based protein identification method to address this issue. Our method applies a new two-stage algorithmic framework that incorporates (i) spectra library searching and (ii) a more sophisticated scoring model. In the first stage, fast spectra library searching and simplified database searching are utilised to determine a reduced search space, which in the second stage is comprehensively explored to find the most likely protein and its modifications. Evaluated on large public datasets, our method is shown to identify more proteins and protein modifications than other popular protein identification engines.","PeriodicalId":350407,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132757505","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

PUPPI: A pathway analysis method using protein-protein interaction network for case-control data PUPPI:一种利用蛋白-蛋白相互作用网络获取病例对照数据的途径分析方法

2013 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2013-04-16 DOI: 10.1109/CIBCB.2013.6595415

R. Chung

引用次数: 3