Researchers continually upload data into public repositories at a rapid pace, yet utilize few common standards for annotation, making it close to impossible to compare or associate data across studies. To address this problem, we will develop a defined meta- data model and build an integrated system called Phenotype Discovery (PhD) that enables researchers to query and find genomic studies of interest in public repositories as well as upload new data into our database (sdGaP), in a standardized manner. A Query Interpreter (QI) will utilize text mining and natural language processing techniques to map free text into concepts in biomedical ontologies, allowing non-structured queries to be answered efficiently. In Phase I of the project, we will develop a proof-of-concept system that can retrospectively structure phenotypic descriptions in dbGaP, and will work with domain experts in pneumology to build use cases and evaluate the automated mappings. In Phase II of the project, we will extend the domain expertise to cardiology, hematology, and sleep disorders to build a more comprehensive system, expanding the phenotype annotation to transcriptome databases, and integrating a flexible automated genotype annotation tool for sdGaP. We will develop a user-friendly interface to prospectively assist researchers in uploading their data with standardized phenotypic annotations. We will provide the tool for free from our website and continuously improve its quality, based on user feedback and usage data.
Phenotype Discovery (PhD) represents a novel, automated system to describe the characteristics of patients whose genetic information is available in public data repositories, without compromising their privacy. This initiative is greatly needed so that more researchers can make use of data collected from projects funded by public agencies. PhD uses new methodology for natural language processing and semantic integration to interpret the narrative text as well as variables and their values from studies in genomic databases. Standardized terminologies will be utilized to ensure that data can be analyzed across different studies.
|Doan, Son; Lin, Ko-Wei; Conway, Mike et al. (2014) PhenDisco: phenotype discovery system for the database of genotypes and phenotypes. J Am Med Inform Assoc 21:31-6|
|Hinske, Ludwig Christian; França, Gustavo S; Torres, Hugo A M et al. (2014) miRIAD-integrating microRNA inter- and intragenic data. Database (Oxford) 2014:|
|Jiang, Xiaoqian; Ji, Zhanglong; Wang, Shuang et al. (2013) Differential-Private Data Publishing Through Component Analysis. Trans Data Priv 6:19-34|
|Li, Pinghao; Wang, Shuang; Kim, Jihoon et al. (2013) DNA-COMPACT: DNA COMpression based on a pattern-aware contextual modeling technique. PLoS One 8:e80377|
|Roozgard, Aminmohammad; Barzigar, Nafise; Wang, Shuang et al. (2013) Nucleotide sequence alignment using sparse coding and belief propagation. Conf Proc IEEE Eng Med Biol Soc 2013:588-91|
|Jiang, Xiaoqian; Sarwate, Anand D; Ohno-Machado, Lucila (2013) Privacy technology to support data sharing for comparative effectiveness research: a systematic review. Med Care 51:S58-65|
|Ross, Mindy K; Lin, Ko-Wei; Truong, Karen et al. (2013) Text Categorization of Heart, Lung, and Blood Studies in the Database of Genotypes and Phenotypes (dbGaP) Utilizing n-grams and Metadata Features. Biomed Inform Insights 6:35-45|
|Alipanah, Neda; Lin, Ko-Wei; Venkatesh, Vinay et al. (2013) Phenotype Information Retrieval for Existing GWAS Studies. AMIA Jt Summits Transl Sci Proc 2013:4-8|
|Lin, Ko-Wei; Tharp, Melissa; Conway, Mike et al. (2013) Feasibility of using Clinical Element Models (CEM) to standardize phenotype variables in the database of genotypes and phenotypes (dbGaP). PLoS One 8:e76384|
|Ohno-Machado, Lucila (2012) To share or not to share: that is not the question. Sci Transl Med 4:165cm15|