AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Monte Carlo Method

Showing 301 to 310 of 314 articles

Clear Filters

DriverML: a machine learning algorithm for identifying driver genes in cancer sequencing studies.

Nucleic acids research
Although rapid progress has been made in computational approaches for prioritizing cancer driver genes, research is far from achieving the ultimate goal of discovering a complete catalog of genes truly associated with cancer. Driver gene lists predic...

Machine learning in multiexposure laser speckle contrast imaging can replace conventional laser Doppler flowmetry.

Journal of biomedical optics
Laser speckle contrast imaging (LSCI) enables video rate imaging of blood flow. However, its relation to tissue blood perfusion is nonlinear and depends strongly on exposure time. By contrast, the perfusion estimate from the slower laser Doppler flow...

Virtual Screening of Anti-Cancer Compounds: Application of Monte Carlo Technique.

Anti-cancer agents in medicinal chemistry
Possibility and necessity of standardization of predictive models for anti-cancer activity are discussed. The hypothesis about rationality of common quantitative analysis of anti-cancer activity and carcinogenicity is developed. Potential of optimal ...

Machine learning approach for rapid and accurate estimation of optical properties using spatial frequency domain imaging.

Journal of biomedical optics
Fast estimation of optical properties from reflectance measurements at two spatial frequencies could pave way for real-time, wide-field and quantitative mapping of vital signs of tissues. We present a machine learning-based approach for estimating op...

Efficient estimation of subdiffusive optical parameters in real time from spatially resolved reflectance by artificial neural networks.

Optics letters
Subdiffusive reflectance captured at short source-detector separations provides increased sensitivity to the scattering phase function and hence allows superficial probing of the tissue ultrastructure. Consequently, estimation of subdiffusive optical...

Data-Adaptive Estimation for Double-Robust Methods in Population-Based Cancer Epidemiology: Risk Differences for Lung Cancer Mortality by Emergency Presentation.

American journal of epidemiology
In this paper, we propose a structural framework for population-based cancer epidemiology and evaluate the performance of double-robust estimators for a binary exposure in cancer mortality. We conduct numerical analyses to study the bias and efficien...

Planning chemical syntheses with deep neural networks and symbolic AI.

Nature
To plan the syntheses of small organic molecules, chemists use retrosynthesis, a problem-solving technique in which target molecules are recursively transformed into increasingly simpler precursors. Computer-aided retrosynthesis would be a valuable t...

Know When You Don't Know: A Robust Deep Learning Approach in the Presence of Unknown Phenotypes.

Assay and drug development technologies
Deep convolutional neural networks show outstanding performance in image-based phenotype classification given that all existing phenotypes are presented during the training of the network. However, in real-world high-content screening (HCS) experimen...

Moisture content prediction in poultry litter using artificial intelligence techniques and Monte Carlo simulation to determine the economic yield from energy use.

Journal of environmental management
The objective of this study is to determine the economic return of poultry litter combustion in boilers to produce bioenergy (thermal and electrical), as this biomass has a high-energy potential due to its component elements, using fuzzy logic to pre...