Glass Box and Black Box Machine Learning Approaches to Exploit Compositional Descriptors of Molecules in Drug Discovery and Aid the Medicinal Chemist.

The synthetic medicinal chemist plays a vital role in drug discovery. Today there are AI tools to guide next syntheses, but many are "Black Boxes" (BB). One learns little more than the prediction made.

Principles of Quantum Mechanics for Artificial Intelligence in medicine. Discussion with reference to the Quantum Universal Exchange Language (Q-UEL).

This paper reviews some basic principles of Quantum Mechanics, Quantum Computing, and Artificial Intelligence in terms of a specific unifying theme. This theme relates to the hyperbolic or split-complex imaginary numbers and their equivalent matrices, rediscovered by Dirac, and the underlying mathematics of the previously described Q-UEL language based on them. Hyperbolic imaginary numbers h have the property hh = +1: contrast the more familiar i such that ii = -1.

De novo protein folding on computers. Benefits and challenges.

There has been recent success in prediction of the three-dimensional folded native structures of proteins, most famously by the AlphaFold Algorithm running on Google's/Alphabet's DeepMind computer. However, this largely involves machine learning of protein structures and is not a de novo protein structure prediction method for predicting three-dimensional structures from amino acid residue sequences. A de novo approach would be based almost entirely on general principles of energy and entropy that govern protein folding energetics, and importantly do so without the use of the amino acid sequences and structural features of other proteins.

Mining real-world high dimensional structured data in medicine and its use in decision support. Some different perspectives on unknowns, interdependency, and distinguishability.

There are many difficulties in extracting and using knowledge for medical analytic and predictive purposes from Real-World Data, even when the data is already well structured in the manner of a large spreadsheet. Preparative curation and standardization or "normalization" of such data involves a variety of chores but underlying them is an interrelated set of fundamental problems that can in part be dealt with automatically during the datamining and inference processes. These fundamental problems are reviewed here and illustrated and investigated with examples.

Computers and preventative diagnosis. A survey with bioinformatics examples of mitochondrial small open reading frame peptides as portents of a new generation of powerful biomarkers.

The present brief survey is to alert developers in datamining, machine learning, inference methods, and other approaches in relation to diagnostic, predictive, and risk assessment medicine about a relatively new class of bioactive messaging peptides in which there is escalating interest. They provide patterns of communication and cross-chatter about states of health and disease within and, importantly, between cells (they also appear extracellularly in biological fluids). This chatter needs to be analyzed somewhat in the manner of the decryption of the Enigma code in the Second World War.

Testing machine learning techniques for general application by using protein secondary structure prediction. A brief survey with studies of pitfalls and benefits using a simple progressive learning approach.

Many researchers have recently used the prediction of protein secondary structure (local conformational states of amino acid residues) to test advances in predictive and machine learning technology such as Neural Net Deep Learning. Protein secondary structure prediction continues to be a helpful tool in research in biomedicine and the life sciences, but it is also extremely enticing for testing predictive methods such as neural nets that are intended for different or more general purposes. A complication is highlighted here for researchers testing their methods for other 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).

Studies in the use of data mining, prediction algorithms, and a universal exchange and inference language in the analysis of socioeconomic health data.

While clinical and biomedical information in digital form has been escalating, it is socioeconomic factors that are important determinants of health on the national and global scale. We show how collective use of data mining and prediction algorithms to analyze socioeconomic population health data can stand beside classical correlation analysis in routine data analysis. The underlying theoretical basis is the Dirac notation and algebra that is a scientific standard but unusual outside of the physical sciences, combined with a theory of expected information first developed for analyzing sparse data but still largely confined to bioinformatics.

Bidirectional General Graphs for inference. Principles and implications for medicine.

Probabilistic inference methods require a more general and realistic description of the world as a Bidirectional General Graph (BGG). While in its original form the Bayes Net (BN) has been promoted as a predictive tool, it is more immediately a way of testing a hypothesis or model about interactions in a system usually considered on a causal basis. Once established, the model can be used in a predictive way, but the problem here is that for a traditional BN the hypotheses or models that can be formed are limited to the Directed Acyclic Graph (DAG) by definition.

Studies in using a universal exchange and inference language for evidence based medicine. Semi-automated learning and reasoning for PICO methodology, systematic review, and environmental epidemiology.

The Q-UEL language of XML-like tags and the associated software applications are providing a valuable toolkit for Evidence Based Medicine (EBM). In this paper the already existing applications, data bases, and tags are brought together with new ones. The particular Q-UEL embodiment used here is the BioIngine.

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.

Data-mining to build a knowledge representation store for clinical decision support. Studies on curation and validation based on machine performance in multiple choice medical licensing examinations.

Extracting medical knowledge by structured data mining of many medical records and from unstructured data mining of natural language source text on the Internet will become increasingly important for clinical decision support. Output from these sources can be transformed into large numbers of elements of knowledge in a Knowledge Representation Store (KRS), here using the notation and to some extent the algebraic principles of the Q-UEL Web-based universal exchange and inference language described previously, rooted in Dirac notation from quantum mechanics and linguistic theory. In a KRS, semantic structures or statements about the world of interest to medicine are analogous to natural language sentences seen as formed from noun phrases separated by verbs, prepositions and other descriptions of relationships.

Implementation of a web based universal exchange and inference language for medicine: Sparse data, probabilities and inference in data mining of clinical data repositories.

We extend Q-UEL, our universal exchange language for interoperability and inference in healthcare and biomedicine, to the more traditional fields of public health surveys. These are the type associated with screening, epidemiological and cross-sectional studies, and cohort studies in some cases similar to clinical trials. There is the challenge that there is some degree of split between frequentist notions of probability as (a) classical measures based only on the idea of counting and proportion and on classical biostatistics as used in the above conservative disciplines, and (b) more subjectivist notions of uncertainty, belief, reliability, or confidence often used in automated inference and decision support systems.

POPPER, a simple programming language for probabilistic semantic inference in medicine.

Our previous reports described the use of the Hyperbolic Dirac Net (HDN) as a method for probabilistic inference from medical data, and a proposed probabilistic medical Semantic Web (SW) language Q-UEL to provide that data. Rather like a traditional Bayes Net, that HDN provided estimates of joint and conditional probabilities, and was static, with no need for evolution due to "reasoning". Use of the SW will require, however, (a) at least the semantic triple with more elaborate relations than conditional ones, as seen in use of most verbs and prepositions, and (b) rules for logical, grammatical, and definitional manipulation that can generate changes in the inference net.

Suggestions for a web based universal exchange and inference language for medicine. Continuity of patient care with PCAST disaggregation.

We describe here the applications of our recently proposed Q-UEL language to continuity of patient care between physicians, specialists and institutions as mediated via the Internet, giving examples derived from HL7 CDA and VistA of particular interest to workflow. Particular attention is given to the Universal Exchange Language for healthcare as requested by the US President׳s Council of Advisors on Science and Technology (PCAST) released in December 2010, especially in regard to disaggregation of the patient record on the Internet. To illustrate many features and options, one of our most elaborate configurations combining them, for disaggregation and reaggregation, is described.

Hyperbolic Dirac Nets for medical decision support. Theory, methods, and comparison with Bayes Nets.

We recently introduced the concept of a Hyperbolic Dirac Net (HDN) for medical inference on the grounds that, while the traditional Bayes Net (BN) is popular in medicine, it is not suited to that domain: there are many interdependencies such that any "node" can be ultimately conditional upon itself. A traditional BN is a directed acyclic graph by definition, while the HDN is a bidirectional general graph closer to a diffuse "field" of influence. Cycles require bidirectionality; the HDN uses a particular type of imaginary number from Dirac׳s quantum mechanics to encode it.

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.

A universal exchange language for healthcare.

We have defined a Universal Exchange Language (UEL) for healthcare that takes a green field approach to the development of a novel "XML-like" language. We consider here what given a free hand might mean: a UEL that incorporates an advanced mathematical foundation that uses Dirac's notation and algebra. For consented and public information, it allows probabilistic inference from UEL semantic web triplet tags.

Drug discovery using very large numbers of patents: general strategy with extensive use of match and edit operations.

A patent data base of 6.7 million compounds generated by a very high performance computer (Blue Gene) requires new techniques for exploitation when extensive use of chemical similarity is involved. Such exploitation includes the taxonomic classification of chemical themes, and data mining to assess mutual information between themes and companies.

Links between quantum physics and thought.

Quantum mechanics (QM) provides a variety of ideas that can assist in developing Artificial Intelligence for healthcare, and opens the possibility of developing a unified system of Best Practice for inference that will embrace both QM and classical inference. Of particular interest is inference in the hyperbolic-complex plane, the counterpart of the normal i-complex plane of basic QM. There are two reasons.

Towards intelligent Internet-roaming agents for mining and inference from medical data.

This paper is effectively subtitled "Considerations of Requirements for Programmable Laws of Probabilistic Higher Order Logical Thought". Why such a need? Issues such as privacy, security, bandwidth, and computational power demand not a central analyzing agency, but roaming agents to analyze the global explosion of medical data in many hundreds of petabytes distributed across many sites. They will send back only the conclusions, not the source data.

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 new physician as unwitting quantum mechanic: is adapting Dirac's inference system best practice for personalized medicine, genomics, and proteomics?

What is the Best Practice for automated inference in Medical Decision Support for personalized medicine? A known system already exists as Dirac's inference system from quantum mechanics (QM) using bra-kets and bras where A and B are states, events, or measurements representing, say, clinical and biomedical rules. Dirac's system should theoretically be the universal best practice for all inference, though QM is notorious as sometimes leading to bizarre conclusions that appear not to be applicable to the macroscopic world of everyday world human experience and medical practice. It is here argued that this apparent difficulty vanishes if QM is assigned one new multiplication function @, which conserves conditionality appropriately, making QM applicable to classical inference including a quantitative form of the predicate calculus.

Data mining and clinical data repositories: Insights from a 667,000 patient data set.

Clinical repositories containing large amounts of biological, clinical, and administrative data are increasingly becoming available as health care systems integrate patient information for research and utilization objectives. To investigate the potential value of searching these databases for novel insights, we applied a new data mining approach, HealthMiner, to a large cohort of 667,000 inpatient and outpatient digital records from an academic medical system. HealthMiner approaches knowledge discovery using three unsupervised methods: CliniMiner, Predictive Analysis, and Pattern Discovery.