AIMC Topic: Systems Analysis

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Development and Applications of Interoperable Biomedical Ontologies for Integrative Data and Knowledge Representation and Multiscale Modeling in Systems Medicine.

Methods in molecular biology (Clifton, N.J.)
The data FAIR Guiding Principles state that all data should be Findable, Accessible, Interoperable, and Reusable. Ontology is critical to data integration, sharing, and analysis. Given thousands of ontologies have been developed in the era of artific...

Deep learning in systems medicine.

Briefings in bioinformatics
Systems medicine (SM) has emerged as a powerful tool for studying the human body at the systems level with the aim of improving our understanding, prevention and treatment of complex diseases. Being able to automatically extract relevant features nee...

ASN-ASAS SYMPOSIUM: FUTURE OF DATA ANALYTICS IN NUTRITION: Mathematical modeling in ruminant nutrition: approaches and paradigms, extant models, and thoughts for upcoming predictive analytics1,2.

Journal of animal science
This paper outlines typical terminology for modeling and highlights key historical and forthcoming aspects of mathematical modeling. Mathematical models (MM) are mental conceptualizations, enclosed in a virtual domain, whose purpose is to translate r...

The role of artificial intelligence in orthopaedic surgery.

British journal of hospital medicine (London, England : 2005)
Despite significant advances in orthopaedic surgery, variability still exists between providers and practice locations, and process inefficiencies are found throughout the health care continuum. Evolving technologies, namely artificial intelligence, ...

A Dynamical Systems Perspective on Flexible Motor Timing.

Trends in cognitive sciences
A hallmark of higher brain function is the ability to rapidly and flexibly adjust behavioral responses based on internal and external cues. Here, we examine the computational principles that allow decisions and actions to unfold flexibly in time. We ...

Neural network-based adaptive dynamic surface control for permanent magnet synchronous motors.

IEEE transactions on neural networks and learning systems
This brief considers the problem of neural networks (NNs)-based adaptive dynamic surface control (DSC) for permanent magnet synchronous motors (PMSMs) with parameter uncertainties and load torque disturbance. First, NNs are used to approximate the un...