The aim is to develop and evaluate machine learning methods for generating quantitative parametric maps of effective atomic number (Z), relative electron density (ρ ), mean excitation energy (I ), and relative stopping power (RSP) from clinical dual...
Artificial intelligence and its machine learning (ML) capabilities are very promising technologies for dermatology and other visually oriented fields due to their power in pattern recognition. Understandably, many physicians distrust replacing clinic...
IEEE/ACM transactions on computational biology and bioinformatics
Jun 7, 2018
Modeling and simulation techniques have demonstrated success in studying biological systems. As the drive to better capture biological complexity leads to more sophisticated simulators, it becomes challenging to perform statistical analyses that help...
Neural networks : the official journal of the International Neural Network Society
Jun 7, 2018
The excellent performance of deep neural networks has enabled us to solve several automatization problems, opening an era of autonomous devices. However, current deep net architectures are heavy with millions of parameters and require billions of flo...
The possibility of using the atomic coordinates of protein-ligand complexes to assess binding affinity has a beneficial impact in the early stages of drug development and design. From the computational view, the creation of reliable scoring functions...
Radiological reporting generates a large amount of free-text clinical narratives, a potentially valuable source of information for improving clinical care and supporting research. The use of automatic techniques to analyze such reports is necessary t...
Machine learning, a collection of data-analytical techniques aimed at building predictive models from multi-dimensional datasets, is becoming integral to modern biological research. By enabling one to generate models that learn from large datasets an...
Identification of risk factors of treatment resistance may be useful to guide treatment selection, avoid inefficient trial-and-error, and improve major depressive disorder (MDD) care. We extended the work in predictive modeling of treatment resistant...
BACKGROUND: Prevention of persistent pain following breast cancer surgery, via early identification of patients at high risk, is a clinical need. Supervised machine-learning was used to identify parameters that predict persistence of significant pain...
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