2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)最新文献_第5页

Indoor signage detection based on saliency map and bipartite graph matching 基于显著性图和二部图匹配的室内标识检测

2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW) Pub Date : 2011-11-12 DOI: 10.1109/BIBMW.2011.6112422

Shuihua Wang, Yingli Tian

引用次数: 17

Modular neural network model based foetal state classification 基于模块化神经网络模型的胎儿状态分类

2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW) Pub Date : 2011-11-12 DOI: 10.1109/BIBMW.2011.6112501

S. Jadhav, S. Nalbalwar, A. Ghatol

{"title":"Modular neural network model based foetal state classification","authors":"S. Jadhav, S. Nalbalwar, A. Ghatol","doi":"10.1109/BIBMW.2011.6112501","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112501","url":null,"abstract":"Cardiotocography (CTG) is a simultaneous recording of foetal heart rate (FHR) and uterine contractions (UC) and it is one of the most common diagnostic techniques to evaluate maternal and foetal well-being during pregnancy and before delivery. Assessment of the foetal state can be verified only after delivery using the foetal (newborn) outcome data. One of the most important features defining the abnormal foetal outcome is low birth weight. This paper proposes a multi-class classification algorithm using Modular neural network (MNN) models. It tries to boost two conflicting main objectives of multi-class classifiers: a high correct classification rate level and a high classification rate for each class. Using a Cardiotocography database of normal, suspect and pathological cases, we trained MNN classifiers with 23 real valued diagnostic features collected from total 2126 foetal CTG signal recordings data from UCI Machine Learning Repository. We used the classification in a detection process. The proposed methodology is presented, which then is tested on UCI Cardiotocography unseen testing data sets. Experimental results are promising paving the way for further research in that direction.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"5 1","pages":"915-917"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78167534","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: 23

Improved compression ratio for model-based ECG compression using differential coding 利用差分编码改进基于模型的心电压缩的压缩比

2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW) Pub Date : 2011-11-12 DOI: 10.1109/BIBMW.2011.6112502

Z. Passand, M. Azarnoosh

引用次数: 0

CUDA-LR: CUDA-accelerated logistic regression analysis tool for gene-gene interaction for genome-wide association study CUDA-LR:用于全基因组关联研究的基因相互作用的cuda加速逻辑回归分析工具

2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW) Pub Date : 2011-11-12 DOI: 10.1109/BIBMW.2011.6112454

Sungyoung Lee, Min-Seok Kwon, Iksoo Huh, T. Park

{"title":"CUDA-LR: CUDA-accelerated logistic regression analysis tool for gene-gene interaction for genome-wide association study","authors":"Sungyoung Lee, Min-Seok Kwon, Iksoo Huh, T. Park","doi":"10.1109/BIBMW.2011.6112454","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112454","url":null,"abstract":"In genome-wide association studies (GWAS), logistic regression (LR) has been most commonly used for finding an association between a disease phenotype and genetic variants such as single nucleotide polymorphism (SNP). Since logistic regression model requires iterative algorithms to get the parameter estimates, its application to GWAS has been limited to the identification of the individual SNPs. Thus, there have been limited applications of LR to multiple SNP analysis including gene-gene interaction analysis in large scale GWAS data. To overcome this computational burden, we developed a logistic regression analysis tool named CUDA-LR, based on the new programming architecture using Graphics Processing Unit (GPU). CUDA-LR supports not only the simple model with single SNP but also more complex model with two SNPs including the interaction. In addition, CUDA-LR provides various parameters to gain more acceleration and perform specified analysis. In the comparison between our analysis and the other methods, CUDA-LR showed almost 700-folds of acceleration and highly reliable results by our GPU specified optimization techniques. We believe that the CUDA-LR now is a useful logistic regression analysis tool for interaction analysis of large scale GWAS datasets.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"47 1","pages":"691-695"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77888398","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

Weighted pooling high-throughput gene expression data sets to maximize the functional coherence of the top rank genes 加权池高通量基因表达数据集，以最大限度地提高顶级基因的功能一致性

2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW) Pub Date : 2011-11-12 DOI: 10.1109/BIBMW.2011.6112550

Xiaodong Zhou, E. George

{"title":"Weighted pooling high-throughput gene expression data sets to maximize the functional coherence of the top rank genes","authors":"Xiaodong Zhou, E. George","doi":"10.1109/BIBMW.2011.6112550","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112550","url":null,"abstract":"In a typical gene expression study with high throughput technique, such as microarray, a biologist usually focuses on the top genes ranked by the P-values to establish gene functional relationship / network, biological pathway, and microbiologically ramifications of the gene's selection. With more datasets publically available, researchers pool data from independent experiments, typically by pooling P-values with equal weight assigned to each dataset, aiming to fetch more biological information from the pooled data. However, the qualities of datasets may vary substantially. Assigning equal weights may not guarantee the optimal result. Applying the equal weights approach to six independent datasets, we observe the top rank genes of data pooled with this approach have less functional coherence than the single dataset that has highest functional coherence. We propose a procedure based on enhanced simulated annealing (ESA) and literature semantic indexing cohesive (LSI-c) analysis to assign optimal weights to datasets so as to maximize the functional coherence of the top rank genes ordered by their pooled P-values. We observe significantly more functional coherence in optimally pooled data than any single dataset or data pooled with equal weights. Identification of top rank genes through our optimal procedure should improve the downstream analysis.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"91 1","pages":"1033-1033"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73054824","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

Viral quasispecies reconstruction from amplicon 454 pyrosequencing reads 从扩增子454焦磷酸测序读取的病毒准种重建

2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW) Pub Date : 2011-11-12 DOI: 10.1109/BIBMW.2011.6112360

Nicholas Mancuso, Bassam Tork, P. Skums, I. Măndoiu, A. Zelikovsky

引用次数: 19

Diagnosis based on decision tree and discrimination analysis for chronic hepatitis b in TCM 基于决策树的慢性乙型肝炎中医诊断与鉴别分析

2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW) Pub Date : 2011-11-12 DOI: 10.1109/BIBMW.2011.6112478

Xiaoyu Chen, Lizhuang Ma, Na Chu, Yiyang Hu

引用次数: 4

An adaptive feature reduction algorithm for cancer classification using wavelet decomposition of serum proteomic and DNA microarray data 基于血清蛋白质组和DNA微阵列数据的小波分解自适应特征约简算法

2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW) Pub Date : 2011-11-12 DOI: 10.1109/BIBMW.2011.6112391

S. Rashid, G. M. Maruf

{"title":"An adaptive feature reduction algorithm for cancer classification using wavelet decomposition of serum proteomic and DNA microarray data","authors":"S. Rashid, G. M. Maruf","doi":"10.1109/BIBMW.2011.6112391","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112391","url":null,"abstract":"A significant challenge in DNA microarray and mass spectrometric data analysis can be attributed to the problem of having a large number of features with a small number of samples or patients in the data set. Particular care is required to deal with such a problem as the low classification accuracy of a model brought about by the small number of features may depict a low predictive capability. To overcome the associated challenges, proper approaches for data preprocessing, feature reduction and identifying the optimal set of features are critical. In this paper, a novel technique has been proposed for feature reduction and cancer classification; which is applicable for two different types of biological data. The proposed method has been implemented on Surface enhanced laser desorption/ionization time-of-flight mass spectrometric (SELDI-TOF-MS) and DNA microarray data sets. This technique is self adaptive and independent of the type data sets. We have developed a two step strategy for feature reduction such as (1) data preprocessing which includes merging and t-testing and (2) wavelet decomposition. For classification purpose, support vector machine (SVM) has been proposed. By evaluating the performance of the proposed algorithm on the two types of datasets it has been shown that the classification accuracy, sensitivity and specificity obtained by the features selected by the proposed method consistently give excellent performance.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"40 1","pages":"305-312"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72936030","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

Prediction of Trans-regulators of Recombination Hotspots in Mouse Genome 小鼠基因组重组热点反式调控因子的预测

2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW) Pub Date : 2011-11-12 DOI: 10.1109/BIBM.2011.77

Min Wu, C. Kwoh, T. Przytycka, Jing Li, Jie Zheng

{"title":"Prediction of Trans-regulators of Recombination Hotspots in Mouse Genome","authors":"Min Wu, C. Kwoh, T. Przytycka, Jing Li, Jie Zheng","doi":"10.1109/BIBM.2011.77","DOIUrl":"https://doi.org/10.1109/BIBM.2011.77","url":null,"abstract":"The regulatory mechanism of recombination is a fundamental problem in genomics, with wide applications in genome wide association studies, birth-defect diseases, molecular evolution, cancer research, etc. In mammalian genomes, recombination events cluster into short genomic regions called ¡§recombination hotspots¡¨. Recently, a 13-mer motif enriched in hotspots is identified as a candidate cis-regulatory element of human recombination hotspots, moreover, a zinc finger protein, PRDM9, binds to this motif and is associated with variation of recombination phenotype in human and mouse genomes, thus is a trans-acting regulator of recombination hotspots. However, this pair of cis and trans-regulators covers only a fraction of hotspots, thus other regulators of recombination hotspots remain to be discovered. In this paper, we propose an approach to predicting additional trans-regulators from DNA-binding proteins by comparing their enrichment of binding sites in hotspots. Applying this approach on newly mapped mouse hotspots genome-wide, we confirmed that PRDM9 is a major trans-regulator of hotspots. In addition, a list of top candidate trans-regulators of mouse hotspots is reported. Using GO analysis we observed that the top genes are enriched with function of his tone modification, highlighting the epigenetic regulatory mechanisms of recombination hotspots.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"104 1","pages":"57-62"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74267351","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

Robust analysis of related samples under the presence of population substructure 种群子结构存在下相关样本的鲁棒性分析

2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW) Pub Date : 2011-11-12 DOI: 10.1109/BIBMW.2011.6112458

Sungkyoung Choi, Sungho Won

引用次数: 0