AIMC Topic: Precision Medicine

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Precision medicine and machine learning towards the prediction of the outcome of potential celiac disease.

Scientific reports
Potential Celiac Patients (PCD) bear the Celiac Disease (CD) genetic predisposition, a significant production of antihuman transglutaminase antibodies, but no morphological changes in the small bowel mucosa. A minority of patients (17%) showed clinic...

Nonparametric machine learning for precision medicine with longitudinal clinical trials and Bayesian additive regression trees with mixed models.

Statistics in medicine
Precision medicine is an active area of research that could offer an analytic paradigm shift for clinical trials and the subsequent treatment decisions based on them. Clinical trials are typically analyzed with the intent of discovering beneficial tr...

Use of artificial intelligence in the imaging of sarcopenia: A narrative review of current status and perspectives.

Nutrition (Burbank, Los Angeles County, Calif.)
Sarcopenia is a muscle disease which previously was associated only with aging, but in recent days it has been gaining more attention for its predictive value in a vast range of conditions and its potential link with overall health. Up to this point,...

Protein glycation - biomarkers of metabolic dysfunction and early-stage decline in health in the era of precision medicine.

Redox biology
Protein glycation provides a biomarker in widespread clinical use, glycated hemoglobin HbA (A1C). It is a biomarker for diagnosis of diabetes and prediabetes and of medium-term glycemic control in patients with established diabetes. A1C is an early-s...

Driving success in personalized medicine through AI-enabled computational modeling.

Drug discovery today
The development of successful drugs is expensive and time-consuming because of high clinical attrition rates. This is caused partially by the rupture seen in the translatability of the drug from the bench to the clinic in the context of personalized ...

Patient journey through cases of depression from claims database using machine learning algorithms.

PloS one
Health insurance and acute hospital-based claims have recently become available as real-world data after marketing in Japan and, thus, classification and prediction using the machine learning approach can be applied to them. However, the methodology ...

Leveraging innovative technology to generate drug response phenotypes for the advancement of biomarker-driven precision dosing.

Clinical and translational science
Although traditional approaches to biomarker discovery have elucidated key molecular markers that have improved drug selection (precision medicine), the discovery of biomarkers that inform optimal dose selection (precision dosing) continues to be a c...

TranSynergy: Mechanism-driven interpretable deep neural network for the synergistic prediction and pathway deconvolution of drug combinations.

PLoS computational biology
Drug combinations have demonstrated great potential in cancer treatments. They alleviate drug resistance and improve therapeutic efficacy. The fast-growing number of anti-cancer drugs has caused the experimental investigation of all drug combinations...

[Applications of artificial intelligence to new drug development].

Annales pharmaceutiques francaises
Artificial intelligence (AI) encompasses technologies recapitulating four dimensions of human intelligence, i.e. sensing, thinking, acting and learning. The convergence of technological advances in those fields allows to integrate massive data and bu...

Radiomics to better characterize small renal masses.

World journal of urology
PURPOSE: Radiomics is a specific field of medical research that uses programmable recognition tools to extract objective information from standard images to combine with clinical data, with the aim of improving diagnostic, prognostic, and predictive ...