AIMC Topic: Machine Learning

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High-Efficiency Machine Learning Method for Identifying Foodborne Disease Outbreaks and Confounding Factors.

Foodborne pathogens and disease
The China National Center for Food Safety Risk Assessment (CFSA) uses the Foodborne Disease Monitoring and Reporting System (FDMRS) to monitor outbreaks of foodborne diseases across the country. However, there are problems of underreporting or errone...

Comparative study of machine learning methods for COVID-19 transmission forecasting.

Journal of biomedical informatics
Within the recent pandemic, scientists and clinicians are engaged in seeking new technology to stop or slow down the COVID-19 pandemic. The benefit of machine learning, as an essential aspect of artificial intelligence, on past epidemics offers a new...

Predicting Energetics Materials' Crystalline Density from Chemical Structure by Machine Learning.

Journal of chemical information and modeling
To expedite new molecular compound development, a long-sought goal within the chemistry community has been to predict molecules' bulk properties of interest a priori to synthesis from a chemical structure alone. In this work, we demonstrate that mach...

Pre-existing and machine learning-based models for cardiovascular risk prediction.

Scientific reports
Predicting the risk of cardiovascular disease is the key to primary prevention. Machine learning has attracted attention in analyzing increasingly large, complex healthcare data. We assessed discrimination and calibration of pre-existing cardiovascul...

RNAmining: A machine learning stand-alone and web server tool for RNA coding potential prediction.

F1000Research
Non-coding RNAs (ncRNAs) are important players in the cellular regulation of organisms from different kingdoms. One of the key steps in ncRNAs research is the ability to distinguish coding/non-coding sequences. We applied seven machine learning algor...

Fp-GAN with Fused Regional Features for the Synthesis of High-Quality Paired Medical Images.

Journal of healthcare engineering
The use of medical image synthesis with generative adversarial networks (GAN) is effective for expanding medical samples. The structural consistency between the synthesized and actual image is a key indicator of the quality of the synthesized image, ...

On the Evaluation of Rhamnolipid Biosurfactant Adsorption Performance on Amberlite XAD-2 Using Machine Learning Techniques.

BioMed research international
Biosurfactants are a series of organic compounds that are composed of two parts, hydrophobic and hydrophilic, and since they have properties such as less toxicity and biodegradation, they are widely used in the food industry. Important applications i...

Predicting Absorption-Distribution Properties of Neuroprotective Phosphine-Borane Compounds Using In Silico Modeling and Machine Learning.

Molecules (Basel, Switzerland)
Phosphine-borane complexes are novel chemical entities with preclinical efficacy in neuronal and ophthalmic disease models. In vitro and in vivo studies showed that the metabolites of these compounds are capable of cleaving disulfide bonds implicated...

A deep neural network-based approach for prediction of mutagenicity of compounds.

Environmental science and pollution research international
We are exposed to various chemical compounds present in the environment, cosmetics, and drugs almost every day. Mutagenicity is a valuable property that plays a significant role in establishing a chemical compound's safety. Exposure and handling of m...

A primer on applying AI synergistically with domain expertise to oncology.

Biochimica et biophysica acta. Reviews on cancer
BACKGROUND: The concurrent growth of large-scale oncology data alongside the computational methods with which to analyze and model it has created a promising environment for revolutionizing cancer diagnosis, treatment, prevention, and drug discovery....