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Thermodynamics

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An 88-milligram insect-scale autonomous crawling robot driven by a catalytic artificial muscle.

Science robotics
The creation of autonomous subgram microrobots capable of complex behaviors remains a grand challenge in robotics largely due to the lack of microactuators with high work densities and capable of using power sources with specific energies comparable ...

Actuation of untethered pneumatic artificial muscles and soft robots using magnetically induced liquid-to-gas phase transitions.

Science robotics
Pneumatic artificial muscles have been widely used in industry because of their simple and relatively high-performance design. The emerging field of soft robotics has also been using pneumatic actuation mechanisms since its formation. However, these ...

Geometry of Energy Landscapes and the Optimizability of Deep Neural Networks.

Physical review letters
Deep neural networks are workhorse models in machine learning with multiple layers of nonlinear functions composed in series. Their loss function is highly nonconvex, yet empirically even gradient descent minimization is sufficient to arrive at accur...

Generalized Born radii computation using linear models and neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Implicit solvent models play an important role in describing the thermodynamics and the dynamics of biomolecular systems. Key to an efficient use of these models is the computation of generalized Born (GB) radii, which is accomplished by ...

mCSM-PPI2: predicting the effects of mutations on protein-protein interactions.

Nucleic acids research
Protein-protein Interactions are involved in most fundamental biological processes, with disease causing mutations enriched at their interfaces. Here we present mCSM-PPI2, a novel machine learning computational tool designed to more accurately predic...

PrankWeb: a web server for ligand binding site prediction and visualization.

Nucleic acids research
PrankWeb is an online resource providing an interface to P2Rank, a state-of-the-art method for ligand binding site prediction. P2Rank is a template-free machine learning method based on the prediction of local chemical neighborhood ligandability cent...

Cost function network-based design of protein-protein interactions: predicting changes in binding affinity.

Bioinformatics (Oxford, England)
MOTIVATION: Accurate and economic methods to predict change in protein binding free energy upon mutation are imperative to accelerate the design of proteins for a wide range of applications. Free energy is defined by enthalpic and entropic contributi...

In silico Prediction of Inhibitory Constant of Thrombin Inhibitors Using Machine Learning.

Combinatorial chemistry & high throughput screening
BACKGROUND: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors.

Synthesis of MnFeO and MnO magnetic nano-composites with enhanced properties for adsorption of Cr(VI): artificial neural network modeling.

Water science and technology : a journal of the International Association on Water Pollution Research
This study reports adsorptive removal of Cr(VI) by magnetic manganese ferrite and manganese oxide nano-particles (MnF-MO-NPs) composite from aqueous media. The X-ray diffraction pattern of MnF-MO-NPs revealed a polycrystalline nature with nanoscale c...

Applications of response surface methodology and artificial neural network for decolorization of distillery spent wash by using activated Piper nigrum.

Journal of environmental biology
Ethanol production from sugarcane molasses yields large volume of highly colored spent wash as effluent. This color is imparted by the recalcitrant melanoidin pigment produced due to the Maillard reaction. In the present work, decolourization of mela...