2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)最新文献_第4页

A fast and noise-adaptive rough-fuzzy hybrid algorithm for medical image segmentation 一种快速、自适应噪声的粗糙模糊混合医学图像分割算法

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706602

A. Srivastava, Abhinav Asati, M. Bhattacharya

{"title":"A fast and noise-adaptive rough-fuzzy hybrid algorithm for medical image segmentation","authors":"A. Srivastava, Abhinav Asati, M. Bhattacharya","doi":"10.1109/BIBM.2010.5706602","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706602","url":null,"abstract":"An Accurate, Fast and Noise-Adaptive segmentation of Brain MR Images for clinical Analysis is a challenging problem. An improved Hybrid Clustering Algorithm is presented here, which integrates the concept of recently popularized Rough Sets and that of Fuzzy Sets. The concept of lower and upper approximations of rough sets is incorporated to handle uncertainty, vagueness, and incompleteness in class definition. For making the segmentation robust to Noise and intensity in-homogeneity, the images are proposed to be pre-processed with a neighbourhood averaging spatial filter. To accelerate the segmentation process, a novel Suppressed Rough Fuzzy C-Means model is presented in which a membership suppression mechanism has been implemented, which creates competition among clusters to speed-up the clustering process. The effectiveness of the presented algorithm along with comparison with other related algorithm has been demonstrated on a set of MR and CT scan images. The results using MRI data show that our method provides better results compared to standard Fuzzy C-Means based algorithms and other modified similar techniques.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"73 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":"114154298","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: 9

Sparse nonnegative matrix factorization with the elastic net 弹性网稀疏非负矩阵分解

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706574

Weixiang Liu, Songfeng Zheng, Sen Jia, L. Shen, Xianghua Fu

引用次数: 5

Metabolomic profiling for biomarker discovery in pancreatic cancer 胰腺癌生物标志物发现的代谢组学分析

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706618

Prabhjit Kaur, K. Sheikh, A. Kirilyuk, Ksenia Kirilyuk, H. Ressom, A. Cheema, B. Kallakury

引用次数: 23

Link-based cluster ensembles for heterogeneous biological data analysis 异构生物数据分析的基于链接的聚类集成

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706631

Natthakan Iam-on, Simon M. Garrett, C. Price, Tossapon Boongoen

{"title":"Link-based cluster ensembles for heterogeneous biological data analysis","authors":"Natthakan Iam-on, Simon M. Garrett, C. Price, Tossapon Boongoen","doi":"10.1109/BIBM.2010.5706631","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706631","url":null,"abstract":"Clinical data has been employed as the major factor for traditional cancer prognosis. However, this classic approach may be ineffective for analyzing morphologically indistinguishable tumor subtypes. As such, the microarray technology emerges as the promising alternative. Despite a large number of microarray studies, the actual clinical application of gene expression data analysis remains limited due to the complexity of generated data and the noise level. Recently, the integrative cluster analysis of both clinical and gene expression data has shown to be an effective alternative to overcome the above-mentioned problems. This paper presents a novel method for using cluster ensembles that is accurate for analyzing heterogeneous biological data. It overcomes the problem of selecting an appropriate clustering algorithm or parameter setting of any potential candidate, especially with a new set of data. The evaluation on real biological and benchmark datasets suggests that the quality of the proposed model is higher than many state-of-the-art cluster ensemble techniques and standard clustering algorithms. Also, its performance is robust to the parameter perturbation, thus providing a reliable and useful means for data analysts and bioinformaticians. Online supplementary is available at http://users.aber.ac.uk/nii07/bibm2010.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"34 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":"125394998","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

A graph-based elastic net for variable selection and module identification for genomic data analysis 基因组数据分析中变量选择和模块识别的基于图形的弹性网络

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706591

Zheng Xia, Xiao-feng Zhou, Wei Chen, Chunqi Chang

{"title":"A graph-based elastic net for variable selection and module identification for genomic data analysis","authors":"Zheng Xia, Xiao-feng Zhou, Wei Chen, Chunqi Chang","doi":"10.1109/BIBM.2010.5706591","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706591","url":null,"abstract":"Recently a network-constraint regression model[1] is proposed to incorporate the prior biological knowledge to perform regression and variable selection. In their method, a l1-norm of the coefficients is defined to impose sparse, meanwhile a Laplacian operation on the biological graph is designed to encourage smoothness of the coefficients along the network. However the grouping effect of their Laplacian smoothness operation only exits when the two connected genes both have positive or negative effects on the response. To overcome this problem, we proposed to apply the Laplacian operation on the absolute values of the coefficients to take account of the positive and negative effects. Here, we call the presented method as graph-based elastic net (GENet) because the proposed method has similar grouping effect with elastic net(ENet)[2] except the smoothness of two coefficients are specified by the network in GENet. Further, an efficient algorithm which has same spirit with LARS [3] is developed to solve our optimization problem. Simulation studies showed that the proposed method has better performance than network-constrained regularization without absolute values. Application to Alzheimer's disease(AD) microarray gene-expression dataset identified several subnetworks on Kyoto Encyclopedia of Genes and Genomes(KEGG) transcriptional pathways that are related to progression of AD. Many of those findings are confirmed by published literatures.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"PP 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":"126357434","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

Cis-regulatory module detection using constraint programming 使用约束规划的顺式调节模块检测

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706592

Tias Guns, Hong Sun, K. Marchal, Siegfried Nijssen

引用次数: 7

Predicting human microRNA-disease associations based on support vector machine 基于支持向量机的人类微rna -疾病关联预测

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706611

Qinghua Jiang, Guohua Wang, Tianjiao Zhang, Yadong Wang

{"title":"Predicting human microRNA-disease associations based on support vector machine","authors":"Qinghua Jiang, Guohua Wang, Tianjiao Zhang, Yadong Wang","doi":"10.1109/BIBM.2010.5706611","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706611","url":null,"abstract":"The identification of disease-related microRNAs is vital for understanding the pathogenesis of disease at the molecular level and may lead to the design of specific molecular tools for diagnosis, treatment and prevention. Experimental identification of disease-related microRNAs poses difficulties. Computational prediction of microRNA-disease associations is one of the complementary means. However, one major issue in microRNA studies is the lack of bioinformatics programs to accurately predict microRNA-disease associations. Herein, we present a machine learning-based approach for distinguishing positive microRNA-disease associations from negative microRNA-disease associations. A set of features was extracted for each positive and negative microRNA-disease association, and a support vector machine (SVM) classifier was trained, which achieved the area under the ROC curve of up to 0.8884 in 10-fold cross-validation procedure, indicating that the SVM-based approach described here can be used to predict potential microRNA-disease associations and formulate testable hypotheses to guide future biological experiments.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"48 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":"125228097","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: 197

A novel reinforcement learning framework for online adaptive seizure prediction 一种新的用于在线自适应癫痫预测的强化学习框架

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706617

Shouyi Wang, W. Chaovalitwongse, Stephen Wong

引用次数: 23

Non-negative matrix and tensor factorization based classification of clinical microarray gene expression data 基于非负矩阵和张量因子分解的临床微阵列基因表达数据分类

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706606

Yifeng Li, A. Ngom

引用次数: 43

Discovering functional gene pathways associated with cancer heterogeneity via sparse supervised learning 通过稀疏监督学习发现与癌症异质性相关的功能基因通路

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI: 10.1109/BIBM.2010.5706572

Shuichi Kawano, Teppei Shimamura, A. Niida, S. Imoto, R. Yamaguchi, Masao Nagasaki, Ryo Yoshida, C. Print, S. Miyano

引用次数: 0