AIMC Topic: Machine Learning

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Prediction of MOF Performance in Vacuum Swing Adsorption Systems for Postcombustion CO Capture Based on Integrated Molecular Simulations, Process Optimizations, and Machine Learning Models.

Environmental science & technology
Postcombustion CO capture and storage (CCS) is a key technological approach to reducing greenhouse gas emission while we transition to carbon-free energy production. However, current solvent-based CO capture processes are considered too energetically...

Identification of the Species Constituents of Maggot Populations Feeding on Decomposing Remains-Facilitation of the Determination of Post Mortem Interval and Time Since Tissue Infestation through Application of Machine Learning and Direct Analysis in Real Time-Mass Spectrometry.

Analytical chemistry
The utilization of entomological specimens such as larvae (maggots) for the estimation of time since oviposition (i.e., egg laying) for post mortem interval determination, or for estimation of time since tissue infestation (in investigations of elder...

Machine learning models for drug-target interactions: current knowledge and future directions.

Drug discovery today
Predicting the binding affinity between compounds and proteins with reasonable accuracy is crucial in drug discovery. Computational prediction of binding affinity between compounds and targets greatly enhances the probability of finding lead compound...

Stroke Prediction with Machine Learning Methods among Older Chinese.

International journal of environmental research and public health
Timely stroke diagnosis and intervention are necessary considering its high prevalence. Previous studies have mainly focused on stroke prediction with balanced data. Thus, this study aimed to develop machine learning models for predicting stroke with...

Could automated machine-learned MRI grading aid epidemiological studies of lumbar spinal stenosis? Validation within the Wakayama spine study.

BMC musculoskeletal disorders
BACKGROUND: MRI scanning has revolutionized the clinical diagnosis of lumbar spinal stenosis (LSS). However, there is currently no consensus as to how best to classify MRI findings which has hampered the development of robust longitudinal epidemiolog...

Machine learning and radiomic phenotyping of lower grade gliomas: improving survival prediction.

European radiology
BACKGROUND AND PURPOSE: Recent studies have highlighted the importance of isocitrate dehydrogenase (IDH) mutational status in stratifying biologically distinct subgroups of gliomas. This study aimed to evaluate whether MRI-based radiomic features cou...

Machine learning analysis of whole mouse brain vasculature.

Nature methods
Tissue clearing methods enable the imaging of biological specimens without sectioning. However, reliable and scalable analysis of large imaging datasets in three dimensions remains a challenge. Here we developed a deep learning-based framework to qua...

Druggability Assessment in TRAPP Using Machine Learning Approaches.

Journal of chemical information and modeling
Accurate protein druggability predictions are important for the selection of drug targets in the early stages of drug discovery. Because of the flexible nature of proteins, the druggability of a binding pocket may vary due to conformational changes. ...