title: Compass: a hybrid method for clinical and biobank data mining.
abstract: We describe a new method for identification of confident associations within large clinical data sets. The method is a hybrid of two existing methods; Self-Organizing Maps and Association Mining. We utilize Self-Organizing Maps as the initial step to reduce the search space, and then apply Association Mining in order to find association rules. We demonstrate that this procedure has a number of advantages compared to traditional Association Mining; it allows for handling numerical variables without a priori binning and is able to generate variable groups which act as "hotspots" for statistically significant associations. We showcase the method on infertility-related data from Danish military conscripts. The clinical data we analyzed contained both categorical type questionnaire data and continuous variables generated from biological measurements, including missing values. From this data set, we successfully generated a number of interesting association rules, which relate an observation with a specific consequence and the p-value for that finding. Additionally, we demonstrate that the method can be used on non-clinical data containing chemical-disease associations in order to find associations between different phenotypes, such as prostate cancer and breast cancer.
authors: Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby, Denmark.
authors: K
authors: K
authors: Krysiak-Baltyn
authors: Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby, Denmark.
authors: T
authors: T
authors: Nordahl Petersen
authors: Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby, Denmark.
authors: K
authors: K
authors: Audouze
authors: University Department of Growth and Reproduction, Rigshospitalet, Copenhagen, Denmark.
authors: Niels
authors: N
authors: Jørgensen
authors: Institute of Preventative Medicine, Bispebjerg and Frederiksberg Hospitals - The Capital Region, Frederiksberg, Denmark.
authors: L
authors: L
authors: Angquist
authors: Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby, Denmark; NNF Center for Protein Research, Health Sciences Faculty, University of Copenhagen, Denmark. Electronic address: brunak@cbs.dtu.dk.
authors: S
authors: S
authors: Brunak
mesh_terms: D000465:Algorithms; D018070:Biological Specimen Banks; D001943:Breast Neoplasms / Q000453:epidemiology; D057225:Data Mining / Q000379:methods*; D003718:Denmark; D005260:Female; D006801:Humans; D007248:Infertility, Male / Q000453:epidemiology; D016247:Information Storage and Retrieval; D008297:Male; D010641:Phenotype; D011471:Prostatic Neoplasms / Q000453:epidemiology; D011795:Surveys and Questionnaires; D043922:Toxicogenetics
keywords: Association mining; Clinical data; Data mining; Rule extraction; Self-Organizing Map