Multidimensionality of microarrays: statistical challenges and (im)possible solutions.
Ontology highlight
ABSTRACT:
SUBMITTER: Michiels S
PROVIDER: S-EPMC5528286 | biostudies-other | 2011 Apr
REPOSITORIES: biostudies-other
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Project description:ObjectiveThe UK Biobank provides a rich collection of longitudinal clinical data coming from different healthcare providers and sources in England, Wales, and Scotland. Although extremely valuable and available to a wide research community, the heterogeneous dataset contains inconsistent medical terminology that is either aligned to several ontologies within the same category or unprocessed. To make these data useful to a research community, data cleaning, curation, and standardization are needed. Significant efforts to perform data reformatting, mapping to any selected ontologies (such as SNOMED-CT) and harmonization are required from any data user to integrate UK Biobank hospital inpatient and self-reported data, data from various registers with primary care (GP) data. The integrated clinical data would provide a more comprehensive picture of one's medical history.Materials and methodsWe evaluated several approaches to map GP clinical Read codes to International Classification of Diseases (ICD) and Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) terminologies. The results were compared, mapping inconsistencies were flagged, a quality category was assigned to each mapping to evaluate overall mapping quality.ResultsWe propose a curation and data integration pipeline for harmonizing diagnosis. We also report challenges identified in mapping Read codes from UK Biobank GP tables to ICD and SNOMED CT.Discussion and conclusionSome of the challenges-the lack of precise one-to-one mapping between ontologies or the need for additional ontology to fully map terms-are general reflecting trade-offs to be made at different steps. Other challenges are due to automatic mapping and can be overcome by leveraging existing mappings, supplemented with automated and manual curation.
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Project description:PurposeThe aim of this study is to report challenges encountered when conducting inter-institutional data collection of obstetric (prenatal and postpartum) and delivery outcomes for research purposes and to propose solutions for enhanced efficiency.MethodsData were collected from women who consented to collection of obstetric and delivery records for an observational study of pregnancy and delivery outcomes following infertility treatment. We analyzed key issues relevant to improving efficiency of obstetric and delivery data collection via quantification of effort (such as number of calls and faxes) required to obtain records from different types of obstetric clinics and hospitals before and after utilization of a revised authorization.ResultsAt time of analysis, records were successfully collected from 320 of the 451 participants who had delivered. The 320 participants received obstetric care at 63 institutions and delivered at 27 hospitals, with 168 (52.5 %) delivering at institutions other than home facility. At time of consent (8 weeks gestation), 155 of 320 (48.5 %) correctly predicted where they would receive obstetric care and 176 (55 %) where they would delivery. Most facilities (nearly 90 %) rejected our original authorization, but most (90 %) accepted the revised authorization described in this manuscript.ConclusionsCollecting records is time-consuming but important as over 50 % of our participants received care outside of the home facility. To efficiently collect outside records, we recommend that researchers interested in maternal and neonatal outcomes consider the guidelines outlined in this manuscript. This report also provides strong evidence of the need to develop data sharing through electronic health records for research purposes.
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Project description:The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 "ML4Microbiome" that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.
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