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Summit on translational bioinformatics最新文献

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Biomolecular Systems of Disease Buried Across Multiple GWAS Unveiled by Information Theory and Ontology. 信息理论与本体揭示隐藏在多个GWAS中的疾病生物分子系统。
Summit on translational bioinformatics Pub Date : 2010-03-01
Younghee Lee, Jianrong Li, Eric Gamazon, James L Chen, Anna Tikhomirov, Nancy J Cox, Yves A Lussier
{"title":"Biomolecular Systems of Disease Buried Across Multiple GWAS Unveiled by Information Theory and Ontology.","authors":"Younghee Lee,&nbsp;Jianrong Li,&nbsp;Eric Gamazon,&nbsp;James L Chen,&nbsp;Anna Tikhomirov,&nbsp;Nancy J Cox,&nbsp;Yves A Lussier","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>A key challenge for genome-wide association studies (GWAS) is to understand how single nucleotide polymorphisms (SNPs) mechanistically underpin complex diseases. While this challenge has been addressed partially by Gene Ontology (GO) enrichment of large list of host genes of SNPs prioritized in GWAS, these enrichment have not been formally evaluated. Here, we develop a novel computational approach anchored in information theoretic similarity, by systematically mining lists of host genes of SNPs prioritized in three adult-onset diabetes mellitus GWAS. The \"gold-standard\" is based on GO associated with 20 published diabetes SNPs' host genes and on our own evaluation. We computationally identify 69 similarity-predicted GO independently validated in all three GWAS (FDR<5%), enriched with those of the gold-standard (odds ratio=5.89, P=4.81e-05), and these terms can be organized by similarity criteria into 11 groupings termed \"biomolecular systems\". Six biomolecular systems were corroborated by the gold-standard and the remaining five were previously uncharacterized. http://lussierlab.org/publications/ITS-GWAS.</p>","PeriodicalId":89276,"journal":{"name":"Summit on translational bioinformatics","volume":"2010 ","pages":"31-5"},"PeriodicalIF":0.0,"publicationDate":"2010-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041547/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29693714","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: 0
Secondary Use of EHR: Data Quality Issues and Informatics Opportunities. 电子病历的二次使用:数据质量问题和信息学机会。
Summit on translational bioinformatics Pub Date : 2010-03-01
Taxiarchis Botsis, Gunnar Hartvigsen, Fei Chen, Chunhua Weng
{"title":"Secondary Use of EHR: Data Quality Issues and Informatics Opportunities.","authors":"Taxiarchis Botsis,&nbsp;Gunnar Hartvigsen,&nbsp;Fei Chen,&nbsp;Chunhua Weng","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Given the large-scale deployment of Electronic Health Records (EHR), secondary use of EHR data will be increasingly needed in all kinds of health services or clinical research. This paper reports some data quality issues we encountered in a survival analysis of pancreatic cancer patients. Using the clinical data warehouse at Columbia University Medical Center in the City of New York, we mined EHR data elements collected between 1999 and 2009 for a cohort of pancreatic cancer patients. Of the 3068 patients who had ICD-9-CM diagnoses for pancreatic cancer, only 1589 had corresponding disease documentation in pathology reports. Incompleteness was the leading data quality issue; many study variables had missing values to various degrees. Inaccuracy and inconsistency were the next common problems. In this paper, we present the manifestations of these data quality issues and discuss some strategies for using emerging informatics technologies to solve these problems.</p>","PeriodicalId":89276,"journal":{"name":"Summit on translational bioinformatics","volume":"2010 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2010-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041534/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29694344","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: 0
Evaluation of an Ontology-anchored Natural Language-based Approach for Asserting Multi-scale Biomolecular Networks for Systems Medicine. 基于本体锚定自然语言的系统医学多尺度生物分子网络断言方法的评价。
Summit on translational bioinformatics Pub Date : 2010-03-01
Tara B Borlawsky, Jianrong Li, Lyudmila Shagina, Matthew G Crowson, Yang Liu, Carol Friedman, Yves A Lussier
{"title":"Evaluation of an Ontology-anchored Natural Language-based Approach for Asserting Multi-scale Biomolecular Networks for Systems Medicine.","authors":"Tara B Borlawsky,&nbsp;Jianrong Li,&nbsp;Lyudmila Shagina,&nbsp;Matthew G Crowson,&nbsp;Yang Liu,&nbsp;Carol Friedman,&nbsp;Yves A Lussier","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>The ability to adequately and efficiently integrate unstructured, heterogeneous datasets, which are incumbent to systems biology and medicine, is one of the primary limitations to their comprehensive analysis. Natural language processing (NLP) and biomedical ontologies are automated methods for capturing, standardizing and integrating information across diverse sources, including narrative text. We have utilized the BioMedLEE NLP system to extract and encode, using standard ontologies (e.g., Cell Type Ontology, Mammalian Phenotype, Gene Ontology), biomolecular mechanisms and clinical phenotypes from the scientific literature. We subsequently applied semantic processing techniques to the structured BioMedLEE output to determine the relationships between these biomolecular and clinical phenotype concepts. We conducted an evaluation that shows an average precision and recall of BioMedLEE with respect to annotating phrases comprised of cell type, anatomy/disease, and gene/protein concepts were 86% and 78%, respectively. The precision of the asserted phenotype-molecular relationships was 75%.</p>","PeriodicalId":89276,"journal":{"name":"Summit on translational bioinformatics","volume":"2010 ","pages":"6-10"},"PeriodicalIF":0.0,"publicationDate":"2010-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041541/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29694346","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: 0
Analysis of eligibility criteria complexity in clinical trials. 临床试验资格标准复杂性分析。
Summit on translational bioinformatics Pub Date : 2010-03-01
Jessica Ross, Samson Tu, Simona Carini, Ida Sim
{"title":"Analysis of eligibility criteria complexity in clinical trials.","authors":"Jessica Ross,&nbsp;Samson Tu,&nbsp;Simona Carini,&nbsp;Ida Sim","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Formal, computer-interpretable representations of eligibility criteria would allow computers to better support key clinical research and care use cases such as eligibility determination. To inform the development of such formal representations for eligibility criteria, we conducted this study to characterize and quantify the complexity present in 1000 eligibility criteria randomly selected from studies in ClinicalTrials.gov. We classified the criteria by their complexity, semantic patterns, clinical content, and data sources. Our analyses revealed significant semantic and clinical content variability. We found that 93% of criteria were comprehensible, with 85% of these criteria having significant semantic complexity, including 40% relying on temporal data. We also identified several domains of clinical content. Using the findings of the study as requirements for computer-interpretable representations of eligibility, we discuss the challenges for creating such representations for use in clinical research and practice.</p>","PeriodicalId":89276,"journal":{"name":"Summit on translational bioinformatics","volume":"2010 ","pages":"46-50"},"PeriodicalIF":0.0,"publicationDate":"2010-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041539/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29693643","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: 0
Analysis of False Positive Errors of an Acute Respiratory Infection Text Classifier due to Contextual Features. 基于上下文特征的急性呼吸道感染文本分类器的假阳性错误分析。
Summit on translational bioinformatics Pub Date : 2010-03-01
Brett R South, Shuying Shen, Wendy W Chapman, Sylvain Delisle, Matthew H Samore, Adi V Gundlapalli
{"title":"Analysis of False Positive Errors of an Acute Respiratory Infection Text Classifier due to Contextual Features.","authors":"Brett R South,&nbsp;Shuying Shen,&nbsp;Wendy W Chapman,&nbsp;Sylvain Delisle,&nbsp;Matthew H Samore,&nbsp;Adi V Gundlapalli","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Text classifiers have been used for biosurveillance tasks to identify patients with diseases or conditions of interest. When compared to a clinical reference standard of 280 cases of Acute Respiratory Infection (ARI), a text classifier consisting of simple rules and NegEx plus string matching for specific concepts of interest produced 569 (4%) false positive (FP) cases. Using instance level manual annotation we estimate the prevalence of contextual attributes and error types leading to FP cases. Errors were due to (1) Deletion errors from abbreviations, spelling mistakes and missing synonyms (57%); (2) Insertion errors from templated document structures such as check boxes, and lists of signs and symptoms (36%) and; (3) Substitution errors from irrelevant concepts and alternate meanings for the same word (6%). We demonstrate that specific concept attributes contribute to false positive cases. These results will inform modifications and adaptations to improve text classifier performance.</p>","PeriodicalId":89276,"journal":{"name":"Summit on translational bioinformatics","volume":"2010 ","pages":"56-60"},"PeriodicalIF":0.0,"publicationDate":"2010-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041533/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29693645","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: 0
A Collaborative Framework for Representation and Harmonization of Clinical Study Data Elements Using Semantic MediaWiki. 基于语义MediaWiki的临床研究数据元素表示与协调的协作框架。
Summit on translational bioinformatics Pub Date : 2010-03-01
Guoqian Jiang, Harold R Solbrig, Dave Iberson-Hurst, Rebecca D Kush, Christopher G Chute
{"title":"A Collaborative Framework for Representation and Harmonization of Clinical Study Data Elements Using Semantic MediaWiki.","authors":"Guoqian Jiang,&nbsp;Harold R Solbrig,&nbsp;Dave Iberson-Hurst,&nbsp;Rebecca D Kush,&nbsp;Christopher G Chute","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Semantic interoperability among terminologies, data elements, and information models is fundamental and critical for sharing information from the scientific bench to the clinical bedside and back among systems. To meet this need, the vision for CDISC is to build a global, accessible electronic library, which enables precise and standardized data element definitions that can be used in applications and studies to improve biomedical research and its link with health care. As a pilot study, we propose a representation and harmonization framework for clinical study data elements and implement a prototype CDISC Shared Health and Research Electronic Library (CSHARE) using Semantic MediaWiki. We report the preliminary observations of how the components worked and the lessons learnt. In summary, the wiki provided a useful prototyping tool from a process standpoint.</p>","PeriodicalId":89276,"journal":{"name":"Summit on translational bioinformatics","volume":"2010 ","pages":"11-5"},"PeriodicalIF":0.0,"publicationDate":"2010-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041544/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29694347","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: 0
Shared Genomics: Developing an accessible integrated analysis platform for Genome-Wide Association Studies. 共享基因组学:为全基因组关联研究开发一个可访问的集成分析平台。
Summit on translational bioinformatics Pub Date : 2010-03-01
David Hoyle, Mark Delderfield, Lee Kitching, Gareth Smith, Iain Buchan
{"title":"Shared Genomics: Developing an accessible integrated analysis platform for Genome-Wide Association Studies.","authors":"David Hoyle,&nbsp;Mark Delderfield,&nbsp;Lee Kitching,&nbsp;Gareth Smith,&nbsp;Iain Buchan","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Increasingly, genome-wide association studies are being used to identify positions within the human genome that have a link with a disease condition. The number of genomic locations studied means that computationally intensive and bioinformatic intensive solutions will have to be used in the analysis of these data sets. In this paper we present an integrated Workbench that provides user-friendly access to parallelized statistical genetics analysis codes for clinical researchers. In addition we biologically annotate statistical analysis results through the reuse of existing bionformatic Taverna workflows.</p>","PeriodicalId":89276,"journal":{"name":"Summit on translational bioinformatics","volume":"2010 ","pages":"18-22"},"PeriodicalIF":0.0,"publicationDate":"2010-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041536/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29693710","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: 0
User requirements for exploring a resource inventory for clinical research. 为临床研究探索资源清单的用户需求。
Summit on translational bioinformatics Pub Date : 2010-03-01
Barbara R Mirel, Zachary Wright, Jessica D Tenenbaum, Paul Saxman, Kevin A Smith
{"title":"User requirements for exploring a resource inventory for clinical research.","authors":"Barbara R Mirel,&nbsp;Zachary Wright,&nbsp;Jessica D Tenenbaum,&nbsp;Paul Saxman,&nbsp;Kevin A Smith","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>The CTSA Inventory of Resources Explorer facilitates searching and finding relevant biomedical resources in this rich, federated inventory. We used efficient and non-traditional formal usability methods to define requirements and to design the Explorer, which may be extended to similar web-based tools.</p>","PeriodicalId":89276,"journal":{"name":"Summit on translational bioinformatics","volume":"2010 ","pages":"31-5"},"PeriodicalIF":0.0,"publicationDate":"2010-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041532/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29693715","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: 0
Concept Discovery for Pathology Reports using an N-gram Model. 使用n图模型的病理报告概念发现。
Summit on translational bioinformatics Pub Date : 2010-03-01
Vincent Yip, Mutlu Mete, Umit Topaloglu, Sinan Kockara
{"title":"Concept Discovery for Pathology Reports using an N-gram Model.","authors":"Vincent Yip,&nbsp;Mutlu Mete,&nbsp;Umit Topaloglu,&nbsp;Sinan Kockara","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>A large amount of valuable information is available in plain text clinical reports. New techniques and technologies are applied to extract information from these reports. One of the leading systems in the cancer community is the Cancer Text Information Extraction System (caTIES), which was developed with caBIG-compliant data structures. caTIES embedded two key components for extracting data: MMTx and GATE. In this paper, an n-gram based framework is proven to be capable of discovering concepts from text reports. MetaMap is used to map medical terms to the National Cancer Institute (NCI) Metathesaurus and the Unified Medical Language System (UMLS) Metathesaurus for verifying legitimate medical data. The final concepts from our framework and caTIES are weighted based on our scoring model. The scores show that, on average, our framework scores higher than caTIES on 848 (36.9%) of reports. Furthermore, 1388 (60.5%) of reports have similar performances on both systems.</p>","PeriodicalId":89276,"journal":{"name":"Summit on translational bioinformatics","volume":"2010 ","pages":"43-7"},"PeriodicalIF":0.0,"publicationDate":"2010-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041542/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29693718","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: 0
VISAGE: A Query Interface for Clinical Research. VISAGE:临床研究查询界面。
Summit on translational bioinformatics Pub Date : 2010-03-01
Guo-Qiang Zhang, Trish Siegler, Paul Saxman, Neil Sandberg, Remo Mueller, Nathan Johnson, Dale Hunscher, Sivaram Arabandi
{"title":"VISAGE: A Query Interface for Clinical Research.","authors":"Guo-Qiang Zhang,&nbsp;Trish Siegler,&nbsp;Paul Saxman,&nbsp;Neil Sandberg,&nbsp;Remo Mueller,&nbsp;Nathan Johnson,&nbsp;Dale Hunscher,&nbsp;Sivaram Arabandi","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>We present the design and implementation of VISAGE (VISual AGgregator and Explorer), a query interface for clinical research. We follow a user-centered development approach and incorporate visual, ontological, searchable and explorative features in three interrelated components: Query Builder, Query Manager and Query Explorer. The Query Explorer provides novel on-line data mining capabilities for purposes such as hypothesis generation or cohort identification. The VISAGE query interface has been implemented as a significant component of Physio-MIMI, an NCRR-funded, multi-CTSA-site pilot project. Preliminary evaluation results show that VISAGE is more efficient for query construction than the i2b2 web-client.</p>","PeriodicalId":89276,"journal":{"name":"Summit on translational bioinformatics","volume":"2010 ","pages":"76-80"},"PeriodicalIF":0.0,"publicationDate":"2010-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041531/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29693650","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: 0
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