Genomic Messaging System Language Including Command Extensions for Clinical Data Categories

Studies of the role of a smart web for precision medicine supported by biobanking

Both the extraction of medical knowledge from data mining many patient records and from authoritative natural language text on the Internet are important for clinical decision support and biomedical research. The samples in biobanks represent a further kind of information repository of recognized increasing importance, so mechanisms being developed for a smart web for medicine should take them into account. While this paper is primarily a review of Quantum Universal Exchange Language as an XML extension to enable a future smart web for healthcare and biomedicine, it is the first time that we have discussed the connection with biobanks and the design of Quantum Universal Exchange Language's XML-like tags to support their use.

Clinical and pharmacogenomic data mining: 4. The FANO program and command set as an example of tools for biomedical discovery and evidence based medicine

The culmination of methodology explored and developed in the preceding three papers is described in terms of the FANO program (also known as CliniMiner) and specifically in terms of the contemporary command set for data mining. This provides a more detailed account of how strategies were implemented in applications described elsewhere, in the previous papers in the series and in a paper on the analysis of 667 000 patient records. Although it is not customary to think of a command set as the output of research, it represents the elements and strategies for data mining biomedical and clinical data with many parameters, that is, in a high dimensional space that requires skilful navigation. The intent is not to promote FANO per se, but to report its science and methodologies. Typical example rules from traditional data mining are that A and B and C associate, or IF A & B THEN C. We need much higher complexity rules for clinical data especially with inclusion of proteomics and genomics. FANO's specific goal is to be able routinely to extract from clinical record repositories and other data not only the complex rules required for biomedical research and the clinical practice of evidence based medicine, but to quantify their uncertainty, that is, their essentially probabilistic nature. The underlying information and number theoretic basis previously described is less of an issue here, being "under the hood", although the fundamental role and use of the Incomplete (generalized) Riemann Zeta Function as a general surprise measure is highlighted, along with its covariance or multivariance analogue, as it appears to be a unique and powerful feature. Another characteristic described is the very general tactic of the metadata operator ':='. It allows decomposition of diverse data types such as trees, spreadsheets, biosequences, sets of objects, amorphous data collections with repeating items, XML structures, and so forth into universally atomic data items with or without metadata, and assists in reconstruction of ontology from the associations and numerical correlations so data mined.