2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops最新文献_第6页

Integration of multiple annotators by aggregating experts and filtering novices 通过聚合专家和过滤新手来集成多个注释器

2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops Pub Date : 2012-10-04 DOI: 10.1109/BIBM.2012.6392657

Ping Zhang, Z. Obradovic

引用次数: 7

A comparison between hierarchical clustering and community detection method in the collection of gene targets for molecular identification of pathogenic fungi 层次聚类法与群落检测法在病原真菌分子鉴定基因靶点采集中的比较

2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops Pub Date : 2012-10-04 DOI: 10.1109/BIBMW.2012.6470234

I. Thapa, S. Bhowmick, D. Bastola

{"title":"A comparison between hierarchical clustering and community detection method in the collection of gene targets for molecular identification of pathogenic fungi","authors":"I. Thapa, S. Bhowmick, D. Bastola","doi":"10.1109/BIBMW.2012.6470234","DOIUrl":"https://doi.org/10.1109/BIBMW.2012.6470234","url":null,"abstract":"Ribosomal RNA sequence is a popular primary molecular target in the diagnosis of many fungal and bacterial infections. More recently a number of other molecular targets like `cytochrome b', `rpoB', `actin' is available in public databases such as GenBank. These sequences could be better alternatives to the popular ribosomal RNA as molecular targets. However, existing computational approaches do not provide a convenient method to collect and make these sequences available for the development of new alternative sequence-based diagnostics that are critical for early detection of infectious agents like fungi. The long-term goal of this study is to develop a computational tool for the rapid identification of infectious agents in biological sample. In the present study, we focus on pre-processing of sequence data in public database and compare a number of clustering approaches to classify currently available DNA sequences into different target genes. We evaluate the correctness of these methods based on the target classification of seven different species of Zygomycetes. Use of a clustering comparison metric has shown that community detection and hierarchical clustering methods are on par with high accuracy.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":"17 1","pages":"756-761"},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77703680","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

Monotonicity functional for a transient mathematical model of oxygen depletion during Photodynamic therapy 光动力治疗过程中氧耗瞬态数学模型的单调泛函

2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops Pub Date : 2012-10-04 DOI: 10.1109/BIBMW.2012.6470263

I. Gkigkitzis

引用次数: 0

Using expression data to help pathway curation 利用表达数据帮助通路管理

2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops Pub Date : 2012-10-04 DOI: 10.1109/BIBMW.2012.6470379

Keith S. Decker, Preeti Anday, Liang Sun, C. Schmidt

引用次数: 2

Mechanism of needle-knife therapy in treating degenerative cervical spondylosis 针刀疗法治疗退行性颈椎病的机制

2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops Pub Date : 2012-10-04 DOI: 10.1109/BIBMW.2012.6470330

Lizhi Chen, Feng Yuan, Xiao-Yan Zhao, Mei-ai Liang, W. Fu

引用次数: 0

An empirical study on knee arthritis remedies 膝关节关节炎治疗的实证研究

2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops Pub Date : 2012-10-04 DOI: 10.1109/BIBMW.2012.6470371

Shun Long, Guo-ming Chen, Weiheng Zhu, Wen-juan Shi

引用次数: 0

A link prediction based unsupervised rank aggregation algorithm for informative gene selection 基于链接预测的无监督秩聚集信息基因选择算法

2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops Pub Date : 2012-10-04 DOI: 10.1109/BIBM.2012.6392697

Kang Li, Nan Du, A. Zhang

{"title":"A link prediction based unsupervised rank aggregation algorithm for informative gene selection","authors":"Kang Li, Nan Du, A. Zhang","doi":"10.1109/BIBM.2012.6392697","DOIUrl":"https://doi.org/10.1109/BIBM.2012.6392697","url":null,"abstract":"Informative Gene Selection is the process of identifying relevant genes that are significantly and differentially expressed in biological procedures. The microarray experiments conducted for this purpose usually implement only less than a hundred of samples to rank the relevance of over thousands of genes. Many irrelevant genes thus may gain statistical importance due to the randomness caused by the small sample problem, while relevant genes may lose focus in the same way. Overcoming such a problem goes beyond what a single microarray dataset can offer and stresses the use of multiple experiment results, which is defined as rank aggregation. In this paper, we propose a novel link prediction based rank aggregation algorithm for the purpose of informative gene selection. Each rank is transferred into a fully connected and weighted network, in which the nodes represent genes and the weights of links stand for priorities between connected nodes (genes). The integration of multiple gene ranks is then formulated as an optimization problem of link prediction on multiple networks, with criterion function favoring the maximization of weighted consensus among each network. We solve the problem through iterative estimation of weights and maximization of consensus among them. In the experimental evaluation, we demonstrate our method on the Prostate Cancer Dataset and compare it with other baseline methods. The results show that our link prediction based rank aggregation method remarkably outperforms all the compared methods, which proves the effectiveness of our framework in finding informative genes from multiple microarray experimental results.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":"68 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77323416","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

Libgapmis: An ultrafast library for short-read single-gap alignment Libgapmis:一个用于短读单间隙对齐的超快库

2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops Pub Date : 2012-10-04 DOI: 10.1109/BIBMW.2012.6470221

Nikolaos S. Alachiotis, S. Berger, T. Flouri, S. Pissis, A. Stamatakis

{"title":"Libgapmis: An ultrafast library for short-read single-gap alignment","authors":"Nikolaos S. Alachiotis, S. Berger, T. Flouri, S. Pissis, A. Stamatakis","doi":"10.1109/BIBMW.2012.6470221","DOIUrl":"https://doi.org/10.1109/BIBMW.2012.6470221","url":null,"abstract":"A broad variety of short-read alignment programmes has been released recently to address the task of mapping tens of millions of short reads to a reference genome, placing emphasis on various aspects of the problem. Although all programmes allow for a small number of alignment mismatches, some of them either perform poorly when allowing gap insertions or they do not allow for gap insertions at all. The seed-and-extend strategy is applied in most of these programmes: after a fast alignment between a fragment of the reference sequence and a high-quality fragment of a short read-the seed-an important problem is to extend the alignment between a relatively short succeeding fragment of the reference sequence and the remaining low-quality fragment of the read allowing a number of mismatches and the insertion of gaps in the alignment. However, the length of the short reads in combination with the gap occurrence frequency observed in various applications suggest that the single-gap alignment of (parts of) those reads is desirable. In this article, we present libgapmis, an ultrafast library for pairwise short-read single-gap alignment including accelerated SSE-based and GPU-based versions. It implements an algorithm, which computes a modified version of the traditional dynamic programming matrix for sequence alignment to solve the above alignment problem. We show that the library functions of the CPU-based version are up to 20x faster compared to competing programmes, while the respective SSE-based and GPU-based versions are up to 6x and llx faster than our CPU-based implementation, respectively. The functions made available via our library can be seamlessly integrated into any short-read alignment pipeline.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":"10 1","pages":"688-695"},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91288929","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

(2) Protein structure determination on demand (2)按需测定蛋白质结构

2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops Pub Date : 2012-10-04 DOI: 10.1109/BIBM.2012.6392662

Ming Li

引用次数: 1

Extracting volumetric information from standard two-dimensional radiological annotations within the clinical workflow 从临床工作流程中的标准二维放射注释中提取体积信息

2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops Pub Date : 2012-10-04 DOI: 10.1109/BIBMW.2012.6470226

Sharmili Roy, M. S. Brown, G. Shih

{"title":"Extracting volumetric information from standard two-dimensional radiological annotations within the clinical workflow","authors":"Sharmili Roy, M. S. Brown, G. Shih","doi":"10.1109/BIBMW.2012.6470226","DOIUrl":"https://doi.org/10.1109/BIBMW.2012.6470226","url":null,"abstract":"In a typical radiological reporting workflow, radiologists make image-based annotations to denote regions of clinical significance or to perform quantitative measurements. Interestingly, virtually all annotation software allow only 2D geometric primitives such as line segments and ellipses; 3D volume annotation is not supported. As a result, when dealing with anatomic entities that have volumetric properties (e.g. tumors, organs), a radiologist must summarize volumetric quantities in a written text-report or use a third party software outside the standard workflow to perform volumetric segmentation. In this paper, we describe an automated method to extract volumes from radiological annotations. Specifically, we describe a clustering method that parses the annotations of unconnected line segments to determine the locations of volumes. We show how this extracted information can be used to bootstrap and accelerate subsequent 3D segmentation while avoiding the need to perform redundant markup or segmentation seeding outside the standard radiological workflow. This 3D data can be utilized to enhance important clinical applications such as radiological reporting, exam summarization and visualization.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":"3 1","pages":"725-731"},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85985853","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