2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)最新文献_第9页

Improving robustness of gene ranking by resampling and permutation based score correction and normalization 通过重采样和基于排列的分数校正和归一化提高基因排序的稳健性

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706607

Feng Yang, K. Mao

引用次数: 6

Improved mammographic mass retrieval performance using multi-view information 利用多视图信息改进乳房x线影像质量检索性能

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706601

Wei Liu, Weidong Xu, Lihua Li, Shuang Li, Huanping Zhao, Juan Zhang

{"title":"Improved mammographic mass retrieval performance using multi-view information","authors":"Wei Liu, Weidong Xu, Lihua Li, Shuang Li, Huanping Zhao, Juan Zhang","doi":"10.1109/BIBM.2010.5706601","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706601","url":null,"abstract":"Breast cancer is the most common malignant disease in women. Mammographic mass retrieval system can help radiologists to improve the diagnostic accuracy by retrieving biopsy-proven masses which are similar with the diagnostic ones. However, although screening mammograms usually consists of two-view(MLO and CC) mammography of the same breast, most breast CAD systems incorporate with image retrieval techniques are based on a single-view principle where query ROI within a view is analyzed independently. In this paper, a mammographic mass retrieval approach based on multi-view information is proposed. In this work, the query example is a multi-view(MLO and CC) mass pair instead of the single view mass in the traditional image retrieval framework. In the experiments, several visual features are used for retrieval evaluation. Both distance similarity measures, such as Euclidean distance, and k-NN regression model based non-distance similarity measures are used for comparison. Experimental study was carried out on a database with 126 biopsy-proven masses(63 mass pairs). Preliminary results showed that multi-view based retrieval approach achieves better retrieval accuracy than single-view based one, especially for the k-NN regression model based similairy metric.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115430883","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

CEO a cloud epistasis computing model in GWAS GWAS中的云上位计算模型

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706542

Zhengkui Wang, Yue Wang, K. Tan, L. Wong, D. Agrawal

引用次数: 11

An accurate classification of native and non-native protein-protein interactions using supervised and semi-supervised learning approaches 使用监督和半监督学习方法对天然和非天然蛋白质相互作用进行准确分类

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706560

Nan Zhao, Bin Pang, C. Shyu, Dmitry Korkin

{"title":"An accurate classification of native and non-native protein-protein interactions using supervised and semi-supervised learning approaches","authors":"Nan Zhao, Bin Pang, C. Shyu, Dmitry Korkin","doi":"10.1109/BIBM.2010.5706560","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706560","url":null,"abstract":"The progress in experimental and computational structural biology has led to a rapid growth of experimentally resolved structures and computational models of proteinprotein interactions. However, distinguishing between the physiological and non-physiological interactions remains a challenging problem. In this work, two related problems of interface classification have been addressed. The first problem is concerned with classification of the physiological and crystal-packing interactions. The second problem deals with the classification of the physiological interactions, or their accurate models, and decoys obtained from the inaccurate docking models. We have defined a universal set of interface features and employed supervised and semi-supervised learning approaches to accurately classify the interactions in both problems. Furthermore, we formulated the second problem as a semi-supervised learning problem and employed a transductive SVM to improve the accuracy of classification. Finally, we showed that using the scoring functions from the obtained classifiers, one can improve the accuracy of the docking methods.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127137279","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

Structural parsimony: Reductions in sequence space 结构简约:序列空间的缩减

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706536

Roberto Blanco

引用次数: 2

Robust hidden semi-Markov modeling of array CGH data 阵列CGH数据的鲁棒隐半马尔可夫建模

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706637

Jiarui Ding, Sohrab P. Shah

{"title":"Robust hidden semi-Markov modeling of array CGH data","authors":"Jiarui Ding, Sohrab P. Shah","doi":"10.1109/BIBM.2010.5706637","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706637","url":null,"abstract":"As an extension to hidden Markov models, the hidden semi-Markov models allow the probability distribution of staying in the same state to be a general distribution. Therefore, hidden semi-Markov models are good at modeling sequences with succession of homogenous zones by choosing appropriate state duration distributions. Hidden semi-Markov models are generative models. Most times they are trained by maximum likelihood estimation. To compensate model mis-specification and provide protection against outliers, hidden semi-Markov models can be trained discriminatively given a labeled training set at the expense of increased training complexity. As an alternative to discriminative training, in this paper, we consider model mis-specification and outliers by adopting robust methods. Specifically, we use Student's t mixture models as the emission distributions of hidden semi-Markov models. The proposed robust hidden semi-Markov models are used to model array based comparative genomic hybridization data. Experiments conducted on the benchmark data from the Coriell cell lines, and the glioblastoma multiforme data illustrate the reliability of the technique.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125862815","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

Truncation of protein sequences for fast profile alignment with application to subcellular localization 截断蛋白质序列用于快速定位与亚细胞定位的应用

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706548

M. Mak, Wei Wang, S. Kung

{"title":"Truncation of protein sequences for fast profile alignment with application to subcellular localization","authors":"M. Mak, Wei Wang, S. Kung","doi":"10.1109/BIBM.2010.5706548","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706548","url":null,"abstract":"We have recently found that the computation time of homology-based subcellular localization can be substantially reduced by aligning profiles up to the cleavage site positions of signal peptides, mitochondrial targeting peptides, and chloro-plast transit peptides [1]. While the method can reduce the profile alignment time by as much as 20 folds, it cannot reduce the computation time spent on creating the profiles. In this paper, we propose a new approach that can reduce both the profile creation time and profile alignment time. In the new approach, instead of cutting the profiles, we shorten the sequences by cutting them at the cleavage site locations. The shortened sequences are then presented to PSI-BLAST to compute the profiles. Experimental results and analysis of profile-alignment score matrices suggest that both profile creation time and profile alignment time can be reduced without sacrificing subcellular localization accuracy. Once a pairwise profile-alignment score matrix has been obtained, a one-vs-rest SVM classifier can be trained. To further reduce the training and recognition time of the classifier, we propose a perturbation discriminant analysis (PDA) technique. It was found that PDA enjoys a short training time as compared to the conventional SVM.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"396 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125924962","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

Sequence and structural features of binding site residues in protein-protein complexes 蛋白质-蛋白质复合物中结合位点残基的序列和结构特征

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706535

M. Gromiha, N. Saranya, S. Selvaraj, B. Jayaram, K. Fukui

引用次数: 0

Toward automatically drawn metabolic pathway atlas with peripheral node abstraction algorithm 利用外周节点提取算法自动绘制代谢途径图谱

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706644

Myungha Jang, A. Rhie, Hyun Seok Park

引用次数: 0

Detection and application of CagA sequence markers for assessing risk factor of gastric cancer caused by Helicobacter pylori CagA序列标记物在幽门螺杆菌致胃癌危险因素评估中的检测及应用

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706614

Chao Zhang, Shunfu Xu, Dong Xu

引用次数: 1