Searching biomedical data sets. Hua Xu, PhD The University of Texas Health Science Center at Houston

Transcription

1 Searching biomedical data sets Hua Xu, PhD The University of Texas Health Science Center at Houston

2 Motivations for biomedical data re-use Improve reproducibility Minimize duplicated efforts on creating similar data sets Enable Big Data Analysis

3 NIH BD2K Data Discovery Index Coordination Consortium (U24) Data and Informatics Working Group (DIWG) report, "Promote Data Sharing Through Central and Federated Catalogues." Development of an NIH BD2K Data Discovery Index Coordination Consortium (U24) HL html An NIH Data Discovery Index (DDI) to allow discovery, access, and citation of biomedical data

4 BioCADDIE - Biomedical and healthcare Data Discovery Indexing Engine (PI Dr. Lucila Ohno-Machado) Goal a sustainable ecosystem for making biomedical data sharing by engaging a broad community of stakeholders Discoverability Access Citation

5 Searching biomedical data sets Technical challenges The complexity of biomedical data The free-text description of biomedical data sets Approaches Standardizing representations of description of biomedical data the pfindr project NLP-enabled Elasticsearch Semantic vector-based search

6 The pfindr (phenotype Finding IN Data Repositories) Project To improve the search of dbgap, a database of genotypes and phenotypes, based on phenotype variables. Funded by NHLBI/NIH PI - Dr. Ohno-Machado Research team - Drs. Kim, Doan, and others

7 NCBI s database of Genotypes and Phenotypes (dbgap) dbgap was developed to archive and distribute the results of studies that have investigated the interaction of genotype and phenotype Until 11/14/2013: top-level studies - 139,238 phenotypes variables - 2,816 datasets - 3,895 analyses 7

8 9/5/13 8

9 Phenotype Variable Standardization Pipeline Phenotype variables Tagger Identify topic and subject of information Categorizer Identify semantic category of phenotypes Topic: Main theme of phenotype variables Subject of information: Bearer of the variable Variable Topic Subject Category Gender of the participant Gender Study subject Demographics CIGARETTES/DAY, EXAM 1 Smoking study subject Smoking History Weight in kg. at baseline weighing patient study subject Clinical Attributes AGE OF LIVING MOTHER Age mother (person) Demographics

10 Phenotype Variable Standardization Pipeline Variable Descriptions Normalization MetaMap Processing Semantic Role Assignment Topic Filtering Variable Categorization Spell out abbreviations and short hand expressions Drop question numbers and other unimportant characters Generate CUIs, concept names, semantic types Semantic types and keywordbased role identification Keep concepts that match SNOMED-CT clinical findings Remove problematic concepts Semantic types and keywordbased categorization Tagger Categorizer 15 semantic categories are selected based on semantic types from MetaMap: Demographics, Medical History, Clinical Attribute, Medication, Lab Tests from two domain experts

11 Phenotype Variable Standardization Pipeline Variable Descriptions Normalization MetaMap Processing Semantic Role Assignment Topic Filtering Variable Categorization 135,608 variables 116,957 phenotypes mapped to Topic 104,172 phenotypes mapped to Category Tagger Categorizer Evaluation: - Random sample of 500 unique phenotypes - Reviewed by 3 domain experts 73% accuracy for topic 71% accuracy for category

12 Search o Term auto-complete o Synonym expansion Search by titles, platform, study PhenDisco system Export to Excel o Selected study metadata o Selected phenotype variables Display o Keyword highlighting o Ranking by relevance o Filter by study metadata o Cross-link related studies 12

13 NLP-enabled elasticsearch A supplement project led by Drs. Hongfang Liu, Serguei Pakhomov, and Hua Xu Elasticsearch for Big Data Distributed Real-time NLP-TAB Visualization Multiple NLP systems

14 Semantic vector-based search A supplement project led by Dr. Trevor Cohen Semantic vector representation for concepts Robustness Scalable and incremental indexing In-memory retrieval and inference How it works for biomedical data search Represent a data set using semantic vector Find similar data sets based on similarity between two semantic vectors of data sets