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. Metastatic bone disease (MBD) is a common complication of advanced cancer and recent research suggests that Endo180 expression is dysregulated through the TGFβ-TGFβR-SMAD2/3 signalling pathway during the invasion of tumour cells in the development of MBD. We here provide a model for the dysregulation of the Endo180 network to demonstrate its vital contribution to bone destruction as well as tumour cell growth. The model consisted of a set of ordinary differential equations and reconstructed variations in the bone cells, resultant bone volume, and biochemical factors involved in the TGFβ-TGFβR-SMAD2/3 signalling pathway over time. The model also investigated the underlying mechanism in which the change of TGFβ affects the TGFβ-TGFβR-SMAD2/3 signalling pathway and the resultant Endo180 expression in osteoblastic and tumour cells. The model links the appearance of tumour cells with the inhibition of TGFβ binding to its receptors on osteoblastic cells, to affect TGFβ-TGFβR-SMAD2/3 signalling and Endo180 expression. Temporal variation in bone cells, bone volume, and the biochemical factors involved in the TGFβ-TGFβR-SMAD2/3 pathway as demonstrated in the model simulations agree with published experimental data. The model can be refined based on further discoveries but allows the influence of Endo180 network dysregulation on bone remodelling in MBD to be established. This model could aid in the development of Endo180 targeted therapies for MBD in the future. OBJECTIVE For small abdominal aortic aneurysms (AAAs), a regular follow-up examination is recommended every 12 months for AAAs of 30-39 mm and every six months for AAAs of 40-55 mm. Follow-up diameters can determine if a patient follows the common growth model of the population. However, the rapid expansion of an AAA, often associated with higher rupture risk, may be overlooked even though it requires surgical intervention. Therefore, the prognosis of abdominal aortic aneurysm growth is clinically important for planning treatment. This study aims to build enhanced Bayesian inference methods to predict maximum aneurysm diameter. METHODS 106 CT scans from 25 Korean AAA patients were retrospectively obtained. A two-step approach based on Bayesian calibration was used, and an exponential abdominal aortic aneurysm growth model (population-based) was specified according to each individual patient's growth (patient-specific) and morphologic characteristics of the aneurysm sac (enhanced). The distribution estimates were obtained using a Markov Chain Monte Carlo (MCMC) sampler. RESULTS The follow-up diameters were predicted satisfactorily (i.e. the true follow-up diameter was in the 95% prediction interval) for 79% of the scans using the population-based growth model, and 83% of the scans using the patient-specific growth model. Among the evaluated geometric measurements, centerline tortuosity was a significant (p = 0.0002) predictor of growth for AAAs with accelerated and stable expansion rates. Using the enhanced prediction model, 86% of follow-up scans were predicted satisfactorily. The average prediction errors of population-based, patient-specific, and enhanced models were ±2.67, ±2.61 and ± 2.79 mm, respectively. CONCLUSION A computational framework using patient-oriented growth models provides useful tools for per-patient basis treatment and enables better prediction of AAA growth. BACKGROUND AND OBJECTIVE Using traditional regression modelling, we have previously demonstrated a positive and strong relationship between paralyzed knee extensors' mechanomyographic (MMG) signals and neuromuscular electrical stimulation (NMES)-assisted knee torque in persons with spinal cord injuries. In the present study, a method of estimating NMES-evoked knee torque from the knee extensors' MMG signals using support vector regression (SVR) modelling is introduced and performed in eight persons with chronic and motor complete spinal lesions. METHODS The model was developed to estimate knee torque from experimentally derived MMG signals and other parameters related to torque production, including the knee angle and stimulation intensity, during NMES-assisted knee extension. RESULTS When the relationship between the actual and predicted torques was quantified using the coefficient of determination (R2), with a Gaussian support vector kernel, the R2 value indicated an estimation accuracy of 95% for the training subset and 94% for the testing subset while the polynomial support vector kernel indicated an accuracy of 92% for the training subset and 91% for the testing subset. For the Gaussian kernel, the root mean square error of the model was 6.28 for the training set and 8.19 for testing set, while the polynomial kernels for the training and testing sets were 7.99 and 9.82, respectively. CONCLUSIONS These results showed good predictive accuracy for SVR modelling, which can be generalized, and suggested that the MMG signals from paralyzed knee extensors are a suitable proxy for the NMES-assisted torque produced during repeated bouts of isometric knee extension tasks. This finding has potential implications for using MMG signals as torque sensors in NMES closed-loop systems and provides valuable information for implementing this method in research and clinical settings. https://www.selleckchem.com/products/smifh2.html