Genomic Messaging System and DNA Mark-Up Language for Information-Based Personalized Medicine with Clinical and Proteome Research Applications

Extension of the Quantum Universal Exchange Language to precision medicine and drug lead discovery. Preliminary example studies using the mitochondrial genome

The Quantum Universal Exchange Language (Q-UEL) based on Dirac notation and algebra from quantum mechanics, along with its associated data mining and Hyperbolic Dirac Net (HDN) for probabilistic inference, has proven to be a useful architectural principle for knowledge management, analysis and prediction systems in medicine. It has been described in several papers; here is described its extension to clinical genomics and precision medicine. Two use cases are studied: (a) bioinformatics in clinical decision support especially for risk for type 2 diabetes using mitochondrial patient DNA sequences, and (b) bioinformatics and computational biology (conformational) research examples related to drug discovery involving the recently discovered class of mitochondrial derived peptides (MDPs). MDPs were surprising when first discovered as coded in small open reading frames (sORFs), and are emerging as having a fundamental role in metabolic control, longevity and disease. This project originally represented a language specification study relating to what information related to genomics is essential or useful to carry, and what processing will be needed. However, novel aspects introduced or discovered include the HDN-like neural nets and their use, along with more established methods, for prediction of type 2 diabetes, and in particular for proposals for over 80 natural MDPs most of which that have not previously been described at the time of the study, as potential drug lead targets. Also, use of many medical records with simulated joining of mtDNA as performance tests led to some insightful observations regarding the behavior of HDN predictions where independent factors are involved.

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.

Suggestions for a Web based universal exchange and inference language for medicine

Mining biomedical and pharmaceutical data generates huge numbers of interacting probabilistic statements for inference, which can be supported by mining Web text sources. This latter can also be probabilistic, in a sense described in this report. However, the diversity of tools for probabilistic inference is troublesome, suggesting a need for a unifying best practice. Physicists often claim that quantum mechanics is the universal best practice for probabilistic reasoning. We discuss how the Dirac notation and algebra suggest the form and algebraic and semantic meaning of XML-like Web tags for a clinical and biomedical universal exchange language formulated to make sense directly to the eye of the physician and biomedical researcher.

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.

Genomic Messaging System Language Including Command Extensions for Clinical Data Categories

This paper, in the area of clinical bioinformatics, highlights relatively efficient means of storing, exchanging, protecting, and searching human and other genomic data, so as to make the data securely accessible to researchers while respecting patient privacy. One important idea is that the GMSL language can be considered as an extension of the way DNA and protein sequences are written so as to carry with them the wishes of the patient in regard to fine-grained consent (as well as retaining the medical experts' cautions, instructions for use, and annotation), and this is carried, whatever environment (e.g., XML) that the data is from or whatever it is going to. At the deepest level, a stream of data expressed in GMSL resembles highly compressed stream of self-checking machine code. For the reader less familiar with the computational aspects, some simple examples illustrate how the raw language looks and works as a raw stream of (interpreted) bytes. The bioinformatics applications are not confined to the clinical domain. This paper completes the initial specification of the language as previously presented and reports on some important extensions including clinical data categories.