AIMC Topic: Machine Learning

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Prediction of the Blood-Brain Barrier (BBB) Permeability of Chemicals Based on Machine-Learning and Ensemble Methods.

Chemical research in toxicology
The ability of chemicals to enter the blood-brain barrier (BBB) is a key factor for central nervous system (CNS) drug development. Although many models for BBB permeability prediction have been developed, they have insufficient accuracy (ACC) and sen...

SMILE: systems metabolomics using interpretable learning and evolution.

BMC bioinformatics
BACKGROUND: Direct link between metabolism and cell and organism phenotype in health and disease makes metabolomics, a high throughput study of small molecular metabolites, an essential methodology for understanding and diagnosing disease development...

The evaluation of COVID-19 prediction precision with a Lyapunov-like exponent.

PloS one
In the field of machine learning, building models and measuring their performance are two equally important tasks. Currently, measures of precision of regression models' predictions are usually based on the notion of mean error, where by error we mea...

Signal-piloted processing and machine learning based efficient power quality disturbances recognition.

PloS one
Significant losses can occur for various smart grid stake holders due to the Power Quality Disturbances (PQDs). Therefore, it is necessary to correctly recognize and timely mitigate the PQDs. In this context, an emerging trend is the development of m...

Recent progress on the prospective application of machine learning to structure-based virtual screening.

Current opinion in chemical biology
As more bioactivity and protein structure data become available, scoring functions (SFs) using machine learning (ML) to leverage these data sets continue to gain further accuracy and broader applicability. Advances in our understanding of the optimal...

Deep Learning-Based Conformal Prediction of Toxicity.

Journal of chemical information and modeling
Predictive modeling for toxicity can help reduce risks in a range of applications and potentially serve as the basis for regulatory decisions. However, the utility of these predictions can be limited if the associated uncertainty is not adequately qu...

A Generative Adversarial Network (GAN) Technique for Internet of Medical Things Data.

Sensors (Basel, Switzerland)
The application of machine learning and artificial intelligence techniques in the medical world is growing, with a range of purposes: from the identification and prediction of possible diseases to patient monitoring and clinical decision support syst...

Predictive modeling for peri-implantitis by using machine learning techniques.

Scientific reports
The purpose of this retrospective cohort study was to create a model for predicting the onset of peri-implantitis by using machine learning methods and to clarify interactions between risk indicators. This study evaluated 254 implants, 127 with and 1...

Supervised binary classification methods for strawberry ripeness discrimination from bioimpedance data.

Scientific reports
Strawberry is one of the most popular fruits in the market. To meet the demanding consumer and market quality standards, there is a strong need for an on-site, accurate and reliable grading system during the whole harvesting process. In this work, a ...

Predicting youth diabetes risk using NHANES data and machine learning.

Scientific reports
Prediabetes and diabetes mellitus (preDM/DM) have become alarmingly prevalent among youth in recent years. However, simple questionnaire-based screening tools to reliably assess diabetes risk are only available for adults, not youth. As a first step ...