Computational method for temporal pattern discovery in biomedical genomic databases.
With the rapid growth of biomedical research databases, opportunities for scientific inquiry have expanded quickly and led to a demand for computational methods that can extract biologically relevant patterns among vast amounts of data. A significant challenge is identifying temporal relationships among genotypic and clinical (phenotypic) data. Few software tools are available for such pattern matching, and they are not interoperable with existing databases. We are developing and validating a novel software method for temporal pattern discovery in biomedical genomics. In this paper, we present an efficient and flexible query algorithm (called TEMF) to extract statistical patterns from time-oriented relational databases. We show that TEMF - as an extension to our modular temporal querying application (Chronus II) - can express a wide range of complex temporal aggregations without the need for data processing in a statistical software package. We show the expressivity of TEMF using example queries from the Stanford HIV Database.
Rafiq
Mohammed I
MI
Stanford Medical Informatics, MSOB X233, Stanford, CA 94305, USA. mirafiq@stanford.edu
O'Connor
Martin J
MJ
Das
Amar K
AK
D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods; D003628:Database Management Systems; D030541:Databases, Genetic; D023281:Genomics / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D016415:Sequence Alignment / Q000379:methods*; D017421:Sequence Analysis / Q000379:methods*; D017385:Sequence Homology; D014584:User-Computer Interface