Journal of clinical bioinformatics最新文献

{"title":"ObTiMA","authors":"H. Stenzhorn","doi":"10.1186/2043-9113-5-S1-S3","DOIUrl":"https://doi.org/10.1186/2043-9113-5-S1-S3","url":null,"abstract":"","PeriodicalId":73663,"journal":{"name":"Journal of clinical bioinformatics","volume":"5 1","pages":"S3 - S3"},"PeriodicalIF":0.0,"publicationDate":"2015-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/2043-9113-5-S1-S3","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65701960","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}

{"title":"tranSMART","authors":"M. Bierkens, Wim van der Linden, K. V. Bochove, W. Weistra, R. Fijneman, Rita Azevedo, J. Boiten, J. Beliën, G. Meijer","doi":"10.1186/2043-9113-5-S1-S9","DOIUrl":"https://doi.org/10.1186/2043-9113-5-S1-S9","url":null,"abstract":"","PeriodicalId":73663,"journal":{"name":"Journal of clinical bioinformatics","volume":"5 1","pages":"S9 - S9"},"PeriodicalIF":0.0,"publicationDate":"2015-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/2043-9113-5-S1-S9","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65702357","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}

{"title":"Recruitment and feasibility tool","authors":"J. V. Leeuwen, A. Bucur, J. Keijser, B. Claerhout, Kristof de Schepper, D. Pérez-Rey, R. Alonso-Calvo","doi":"10.1186/2043-9113-5-S1-S10","DOIUrl":"https://doi.org/10.1186/2043-9113-5-S1-S10","url":null,"abstract":"","PeriodicalId":73663,"journal":{"name":"Journal of clinical bioinformatics","volume":"5 1","pages":"S10 - S10"},"PeriodicalIF":0.0,"publicationDate":"2015-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/2043-9113-5-S1-S10","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65701093","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}

{"title":"TRANSFoRm eCRF","authors":"W. Kuchinke, Theodoros N. Arvanitis","doi":"10.1186/2043-9113-5-S1-S17","DOIUrl":"https://doi.org/10.1186/2043-9113-5-S1-S17","url":null,"abstract":"","PeriodicalId":73663,"journal":{"name":"Journal of clinical bioinformatics","volume":"5 1","pages":"S17 - S17"},"PeriodicalIF":0.0,"publicationDate":"2015-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/2043-9113-5-S1-S17","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65701206","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}

{"title":"TRANSFoRm Query Workbench","authors":"Theodoros N. Arvanitis, W. Kuchinke","doi":"10.1186/2043-9113-5-S1-S16","DOIUrl":"https://doi.org/10.1186/2043-9113-5-S1-S16","url":null,"abstract":"","PeriodicalId":73663,"journal":{"name":"Journal of clinical bioinformatics","volume":"5 1","pages":"S16 - S16"},"PeriodicalIF":0.0,"publicationDate":"2015-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/2043-9113-5-S1-S16","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65701530","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}

{"title":"MOLGENIS catalogue","authors":"M. Swertz, David van Enckevort, Chao Pang","doi":"10.1186/2043-9113-5-S1-S8","DOIUrl":"https://doi.org/10.1186/2043-9113-5-S1-S8","url":null,"abstract":"","PeriodicalId":73663,"journal":{"name":"Journal of clinical bioinformatics","volume":"5 1","pages":"S8 - S8"},"PeriodicalIF":0.0,"publicationDate":"2015-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/2043-9113-5-S1-S8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65701891","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}

{"title":"Clinical decision support systems for improving diagnostic accuracy and achieving precision medicine.","authors":"Christian Castaneda,&nbsp;Kip Nalley,&nbsp;Ciaran Mannion,&nbsp;Pritish Bhattacharyya,&nbsp;Patrick Blake,&nbsp;Andrew Pecora,&nbsp;Andre Goy,&nbsp;K Stephen Suh","doi":"10.1186/s13336-015-0019-3","DOIUrl":"https://doi.org/10.1186/s13336-015-0019-3","url":null,"abstract":"<p><p>As research laboratories and clinics collaborate to achieve precision medicine, both communities are required to understand mandated electronic health/medical record (EHR/EMR) initiatives that will be fully implemented in all clinics in the United States by 2015. Stakeholders will need to evaluate current record keeping practices and optimize and standardize methodologies to capture nearly all information in digital format. Collaborative efforts from academic and industry sectors are crucial to achieving higher efficacy in patient care while minimizing costs. Currently existing digitized data and information are present in multiple formats and are largely unstructured. In the absence of a universally accepted management system, departments and institutions continue to generate silos of information. As a result, invaluable and newly discovered knowledge is difficult to access. To accelerate biomedical research and reduce healthcare costs, clinical and bioinformatics systems must employ common data elements to create structured annotation forms enabling laboratories and clinics to capture sharable data in real time. Conversion of these datasets to knowable information should be a routine institutionalized process. New scientific knowledge and clinical discoveries can be shared via integrated knowledge environments defined by flexible data models and extensive use of standards, ontologies, vocabularies, and thesauri. In the clinical setting, aggregated knowledge must be displayed in user-friendly formats so that physicians, non-technical laboratory personnel, nurses, data/research coordinators, and end-users can enter data, access information, and understand the output. The effort to connect astronomical numbers of data points, including '-omics'-based molecular data, individual genome sequences, experimental data, patient clinical phenotypes, and follow-up data is a monumental task. Roadblocks to this vision of integration and interoperability include ethical, legal, and logistical concerns. Ensuring data security and protection of patient rights while simultaneously facilitating standardization is paramount to maintaining public support. The capabilities of supercomputing need to be applied strategically. A standardized, methodological implementation must be applied to developed artificial intelligence systems with the ability to integrate data and information into clinically relevant knowledge. Ultimately, the integration of bioinformatics and clinical data in a clinical decision support system promises precision medicine and cost effective and personalized patient care. </p>","PeriodicalId":73663,"journal":{"name":"Journal of clinical bioinformatics","volume":"5 ","pages":"4"},"PeriodicalIF":0.0,"publicationDate":"2015-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s13336-015-0019-3","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33183093","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}

{"title":"Semi-automated literature mining to identify putative biomarkers of disease from multiple biofluids.","authors":"Rick Jordan,&nbsp;Shyam Visweswaran,&nbsp;Vanathi Gopalakrishnan","doi":"10.1186/2043-9113-4-13","DOIUrl":"https://doi.org/10.1186/2043-9113-4-13","url":null,"abstract":"<p><strong>Background: </strong>Computational methods for mining of biomedical literature can be useful in augmenting manual searches of the literature using keywords for disease-specific biomarker discovery from biofluids. In this work, we develop and apply a semi-automated literature mining method to mine abstracts obtained from PubMed to discover putative biomarkers of breast and lung cancers in specific biofluids.</p><p><strong>Methodology: </strong>A positive set of abstracts was defined by the terms 'breast cancer' and 'lung cancer' in conjunction with 14 separate 'biofluids' (bile, blood, breastmilk, cerebrospinal fluid, mucus, plasma, saliva, semen, serum, synovial fluid, stool, sweat, tears, and urine), while a negative set of abstracts was defined by the terms '(biofluid) NOT breast cancer' or '(biofluid) NOT lung cancer.' More than 5.3 million total abstracts were obtained from PubMed and examined for biomarker-disease-biofluid associations (34,296 positive and 2,653,396 negative for breast cancer; 28,355 positive and 2,595,034 negative for lung cancer). Biological entities such as genes and proteins were tagged using ABNER, and processed using Python scripts to produce a list of putative biomarkers. Z-scores were calculated, ranked, and used to determine significance of putative biomarkers found. Manual verification of relevant abstracts was performed to assess our method's performance.</p><p><strong>Results: </strong>Biofluid-specific markers were identified from the literature, assigned relevance scores based on frequency of occurrence, and validated using known biomarker lists and/or databases for lung and breast cancer [NCBI's On-line Mendelian Inheritance in Man (OMIM), Cancer Gene annotation server for cancer genomics (CAGE), NCBI's Genes & Disease, NCI's Early Detection Research Network (EDRN), and others]. The specificity of each marker for a given biofluid was calculated, and the performance of our semi-automated literature mining method assessed for breast and lung cancer.</p><p><strong>Conclusions: </strong>We developed a semi-automated process for determining a list of putative biomarkers for breast and lung cancer. New knowledge is presented in the form of biomarker lists; ranked, newly discovered biomarker-disease-biofluid relationships; and biomarker specificity across biofluids.</p>","PeriodicalId":73663,"journal":{"name":"Journal of clinical bioinformatics","volume":"4 ","pages":"13"},"PeriodicalIF":0.0,"publicationDate":"2014-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/2043-9113-4-13","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32800197","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}

{"title":"Extraction of echocardiographic data from the electronic medical record is a rapid and efficient method for study of cardiac structure and function.","authors":"Quinn S Wells,&nbsp;Eric Farber-Eger,&nbsp;Dana C Crawford","doi":"10.1186/2043-9113-4-12","DOIUrl":"https://doi.org/10.1186/2043-9113-4-12","url":null,"abstract":"<p><strong>Background: </strong>Measures of cardiac structure and function are important human phenotypes that are associated with a range of clinical outcomes. Studying these traits in large populations can be time consuming and costly. Utilizing data from large electronic medical records (EMRs) is one possible solution to this problem. We describe the extraction and filtering of quantitative transthoracic echocardiographic data from the Epidemiologic Architecture for Genes Linked to Environment (EAGLE) study, a large, racially diverse, EMR-based cohort (n = 15,863).</p><p><strong>Results: </strong>There were 6,076 echocardiography reports for 2,834 unique adult subjects. Missing data were uncommon with over 90% of data points present. Data irregularities are primarily related to inconsistent use of measurement units and transcriptional errors. The reported filtering method requires manual review of very few data points (<1%), and filtered echocardiographic parameters are similar to published data from epidemiologic populations of similar ethnicity. Moreover, the cohort is comparable in size, and in some cases larger than community-based cohorts of similar race/ethnicity.</p><p><strong>Conclusions: </strong>These results demonstrate that echocardiographic data can be efficiently extracted from EMRs, and suggest that EMR-based cohorts have the potential to make major contributions toward the study of epidemiologic and genotype-phenotype associations for cardiac structure and function in diverse populations.</p>","PeriodicalId":73663,"journal":{"name":"Journal of clinical bioinformatics","volume":"4 ","pages":"12"},"PeriodicalIF":0.0,"publicationDate":"2014-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/2043-9113-4-12","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32713691","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}

{"title":"Meta-analyses of 4 CFTR variants associated with the risk of the congenital bilateral absence of the vas deferens.","authors":"Xuting Xu,&nbsp;Jufen Zheng,&nbsp;Qi Liao,&nbsp;Huiqing Zhu,&nbsp;Hongyan Xie,&nbsp;Huijuan Shi,&nbsp;Shiwei Duan","doi":"10.1186/2043-9113-4-11","DOIUrl":"https://doi.org/10.1186/2043-9113-4-11","url":null,"abstract":"<p><strong>Aims: </strong>The aim of our study was to evaluate the relationship between four CFTR variations and the congenital bilateral absence of the vas deferens (CBAVD).</p><p><strong>Methods: </strong>A systematic search was performed in the literature databases for the case-control studies of CFTR variations with the risk of CBAVD. A total of 29 studies among 1139 controls and 1562 CBAVD patients were gathered for the meta-analyses of three commonly tecsted variations (5T, ΔF508 and M470V) with CBAVD.</p><p><strong>Results: </strong>Our meta-analyses observed significant associations between CBAVD and all the three variations, including 5T (P < 0.001, OR = 8.35, 95% CI = 6.68-10.43), M470V (P = 0.027, OR = 0.74, 95% CI = 0.60-0.91) and ΔF508 (P < 0.001, OR = 22.20, 95% CI = 7.49-65.79).</p><p><strong>Conclusion: </strong>In the current study, we demonstrated a significant association between CFTR variations and CBAVD. Our results showed that the 5T variation was a risk factor of CBAVD in French, Spanish, Japanese, Chinese, Iranian, Indian, Mexican and Egyptian populations. CFTR ΔF508 was another important risk factor in Caucasians, including Slovenians, Canadians, Iranians, and Egyptians. In addition, M470V was a protective factor among French, Chinese, Italian and Iranian populations.</p>","PeriodicalId":73663,"journal":{"name":"Journal of clinical bioinformatics","volume":"4 ","pages":"11"},"PeriodicalIF":0.0,"publicationDate":"2014-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/2043-9113-4-11","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32625383","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}