Proceedings. IEEE International Conference on Bioinformatics and Biomedicine最新文献_第10页

Deep Learning of Tissue Fate Features in Acute Ischemic Stroke. 急性缺血性脑卒中组织命运特征的深度学习。

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2015-11-01 Epub Date: 2015-12-17 DOI: 10.1109/BIBM.2015.7359869

Noah Stier, Nicholas Vincent, David Liebeskind, Fabien Scalzo

引用次数: 47

Integration of multimodal RNA-seq data for prediction of kidney cancer survival. 整合多模态RNA-seq数据预测肾癌生存。

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2015-11-01 DOI: 10.1109/BIBM.2015.7359913

Matt Schwartzi, Martin Parkl, John H Phanl, May D Wang

引用次数: 10

Insight: Semantic Provenance and Analysis Platform for Multi-center Neurology Healthcare Research. Insight:多中心神经保健研究的语义来源和分析平台。

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2015-11-01 DOI: 10.1109/BIBM.2015.7359776

Priya Ramesh, Annan Wei, Elisabeth Welter, Yvan Bamps, Shelley Stoll, Ashley Bukach, Martha Sajatovic, Satya S Sahoo

{"title":"Insight: Semantic Provenance and Analysis Platform for Multi-center Neurology Healthcare Research.","authors":"Priya Ramesh, Annan Wei, Elisabeth Welter, Yvan Bamps, Shelley Stoll, Ashley Bukach, Martha Sajatovic, Satya S Sahoo","doi":"10.1109/BIBM.2015.7359776","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359776","url":null,"abstract":"Insight is a Semantic Web technology-based platform to support large-scale secondary analysis of healthcare data for neurology clinical research. Insight features the novel use of: (1) provenance metadata, which describes the history or origin of patient data, in clinical research analysis, and (2) support for patient cohort queries across multiple institutions conducting research in epilepsy, which is the one of the most common neurological disorders affecting 50 million persons worldwide. Insight is being developed as a healthcare informatics infrastructure to support a national network of eight epilepsy research centers across the U.S. funded by the U.S. Centers for Disease Control and Prevention (CDC). This paper describes the use of the World Wide Web Consortium (W3C) PROV recommendation for provenance metadata that allows researchers to create patient cohorts based on the provenance of the research studies. In addition, the paper describes the use of descriptive logic-based OWL2 epilepsy ontology for cohort queries with \"expansion of query expression\" using ontology reasoning. Finally, the evaluation results for the data integration and query performance are described using data from three research studies with 180 epilepsy patients. The experiment results demonstrate that Insight is a scalable approach to use Semantic provenance metadata for context-based data analysis in healthcare informatics.","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2015 ","pages":"731-736"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/BIBM.2015.7359776","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34393837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

A Hybrid Algorithm for Non-negative Matrix Factorization Based on Symmetric Information Divergence. 基于对称信息散度的非负矩阵分解混合算法。

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2015-11-01 Epub Date: 2015-12-17 DOI: 10.1109/BIBM.2015.7359924

Karthik Devarajan, Nader Ebrahimi, Ehsan Soofi

引用次数: 4

Comparison of an Atomic Model and Its Cryo-EM Image at the Central Axis of a Helix. 螺旋中轴线上原子模型及其低温电镜图像的比较。

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2015-11-01 DOI: 10.1109/BIBM.2015.7359860

Jing He, Stephanie Zeil, Hussam Hallak, Kele McKaig, Julio Kovacs, Willy Wriggers

引用次数: 1

Detection of Hyperperfusion on Arterial Spin Labeling using Deep Learning. 利用深度学习检测动脉自旋标记的高灌注。

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2015-11-01 Epub Date: 2015-12-17 DOI: 10.1109/BIBM.2015.7359870

Nicholas Vincent, Noah Stier, Songlin Yu, David S Liebeskind, Danny Jj Wang, Fabien Scalzo

{"title":"Detection of Hyperperfusion on Arterial Spin Labeling using Deep Learning.","authors":"Nicholas Vincent, Noah Stier, Songlin Yu, David S Liebeskind, Danny Jj Wang, Fabien Scalzo","doi":"10.1109/BIBM.2015.7359870","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359870","url":null,"abstract":"Hyperperfusion detected on arterial spin labeling (ASL) images acquired after acute stroke onset has been shown to correlate with development of subsequent intracerebral hemorrhage. We present in this study a quantitative hyperperfusion detection model that can provide an objective decision support for the interpretation of ASL cerebral blood flow (CBF) maps and rapidly delineate hyperperfusion regions. The detection problem is solved using Deep Learning such that the model relates ASL image patches to the corresponding label (normal or hyperperfused). Our method takes into account the regional intensity values of contralateral hemisphere during the labeling of a pixel. Each input vector is associated to a label corresponding to the presence of hyperperfusion that was manually established by a clinical researcher in Neurology. When compared to the manually established hyperperfusion, the predicted maps reached an accuracy of 97.45 ± 2.49% after crossvalidation. Pattern recognition based on deep learning can provide an accurate and objective measure of hyperperfusion on ASL CBF images and could therefore improve the detection of hemorrhagic transformation in acute stroke patients.","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2015 ","pages":"1322-1327"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/BIBM.2015.7359870","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35431192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Identifying Hypertrophic Cardiomyopathy Patients by Classifying Individual Heartbeats from 12-lead ECG Signals. 从12导联心电图信号中对个体心跳进行分类识别肥厚性心肌病患者。

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2014-11-01 DOI: 10.1109/BIBM.2014.6999159

Quazi Abidur Rahman, Larisa G Tereshchenko, Matthew Kongkatong, Theodore Abraham, M Roselle Abraham, Hagit Shatkay

{"title":"Identifying Hypertrophic Cardiomyopathy Patients by Classifying Individual Heartbeats from 12-lead ECG Signals.","authors":"Quazi Abidur Rahman, Larisa G Tereshchenko, Matthew Kongkatong, Theodore Abraham, M Roselle Abraham, Hagit Shatkay","doi":"10.1109/BIBM.2014.6999159","DOIUrl":"https://doi.org/10.1109/BIBM.2014.6999159","url":null,"abstract":"Test based on electrocardiograms (ECG) that record the heart electrical activity can help in early detection of patients with hypertrophic cardiomyopathy (HCM) where the heart muscle is partially thickened and blood flow is (potentially fatally) obstructed. This paper presents a cardiovascular-patient classifier we developed to identify HCM patients using standard 10-seconds, 12-lead ECG signals. Patients are classified as having HCM if the majority of the heartbeats are recognized as HCM. Thus, the classifier's underlying task is to recognize individual heartbeats segmented from 12-lead ECG signals as HCM beats, where heartbeats from non-HCM cardiovascular patients are used as controls. We extracted 504 morphological and temporal features - both commonly used and newly-developed ones - from ECG signals for heartbeat classification. To assess classification performance, we trained and tested a random forest classifier and a support vector machine classifier using 5-fold cross validation. The patient-classification precision and F-measure of both classifiers are close to 0.85. Recall (sensitivity) and specificity are approximately 0.90. We also conducted feature selection experiments by gradually removing the least informative features; the results show that a relatively small subset of 304 highly informative features can achieve performance measures comparable to that achieved by using the complete set of features.","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2014 ","pages":"224-229"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/BIBM.2014.6999159","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33431174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

High-Performance Computational Analysis of Glioblastoma Pathology Images with Database Support Identifies Molecular and Survival Correlates. 胶质母细胞瘤病理图像的高性能计算分析与数据库支持识别分子和生存相关。

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2013-12-01 DOI: 10.1109/BIBM.2013.6732495

Jun Kong, Fusheng Wang, George Teodoro, Lee Cooper, Carlos S Moreno, Tahsin Kurc, Tony Pan, Joel Saltz, Daniel Brat

{"title":"High-Performance Computational Analysis of Glioblastoma Pathology Images with Database Support Identifies Molecular and Survival Correlates.","authors":"Jun Kong, Fusheng Wang, George Teodoro, Lee Cooper, Carlos S Moreno, Tahsin Kurc, Tony Pan, Joel Saltz, Daniel Brat","doi":"10.1109/BIBM.2013.6732495","DOIUrl":"https://doi.org/10.1109/BIBM.2013.6732495","url":null,"abstract":"In this paper, we present a novel framework for microscopic image analysis of nuclei, data management, and high performance computation to support translational research involving nuclear morphometry features, molecular data, and clinical outcomes. Our image analysis pipeline consists of nuclei segmentation and feature computation facilitated by high performance computing with coordinated execution in multi-core CPUs and Graphical Processor Units (GPUs). All data derived from image analysis are managed in a spatial relational database supporting highly efficient scientific queries. We applied our image analysis workflow to 159 glioblastomas (GBM) from The Cancer Genome Atlas dataset. With integrative studies, we found statistics of four specific nuclear features were significantly associated with patient survival. Additionally, we correlated nuclear features with molecular data and found interesting results that support pathologic domain knowledge. We found that Proneural subtype GBMs had the smallest mean of nuclear Eccentricity and the largest mean of nuclear Extent, and MinorAxisLength. We also found gene expressions of stem cell marker MYC and cell proliferation maker MKI67 were correlated with nuclear features. To complement and inform pathologists of relevant diagnostic features, we queried the most representative nuclear instances from each patient population based on genetic and transcriptional classes. Our results demonstrate that specific nuclear features carry prognostic significance and associations with transcriptional and genetic classes, highlighting the potential of high throughput pathology image analysis as a complementary approach to human-based review and translational research.","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":" ","pages":"229-236"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/BIBM.2013.6732495","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32564580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

Network-based Pathway Enrichment Analysis. 基于网络的通路富集分析。

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2013-01-01 DOI: 10.1109/BIBM.2013.6732493

Lu Liu, Jianhua Ruan

{"title":"Network-based Pathway Enrichment Analysis.","authors":"Lu Liu, Jianhua Ruan","doi":"10.1109/BIBM.2013.6732493","DOIUrl":"https://doi.org/10.1109/BIBM.2013.6732493","url":null,"abstract":"Finding out the associations between an input gene set, such as genes associated with a certain phenotype, and annotated gene sets, such as known pathways, are a very important problem in modern molecular biology. The existing approaches mainly focus on the overlap between the two, and may miss important but subtle relationships between genes. In this paper, we propose a method, NetPEA, by combining the known pathways and high-throughput networks. Our method not only considers the shared genes, but also takes the gene interactions into account. It utilizes a protein-protein interaction network and a random walk procedure to identify hidden relationships between gene sets, and uses a randomization strategy to evaluate the significance for pathways to achieve such similarity scores. Compared with the over-representation based method, our method can identify more relationships. Compared with a state of the art network-based method, EnrichNet, our method not only provides a ranked list of pathways, but also provides the statistical significant information. Importantly, through independent tests, we show that our method likely has a higher sensitivity in revealing the true casual pathways, while at the same time achieve a higher specificity. Literature review of selected results indicates that some of the novel pathways reported by our method are biologically relevant and important.","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":" ","pages":"218-221"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/BIBM.2013.6732493","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32758305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 15

Text Mining Driven Drug-Drug Interaction Detection. 文本挖掘驱动药物-药物相互作用检测。

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2013-01-01 DOI: 10.1109/BIBM.2013.6732517

Su Yan, Xiaoqian Jiang, Ying Chen

引用次数: 24