AIMC Topic: Machine Learning

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Machine learning approaches used to analyze auditory evoked responses from the human auditory brainstem: A systematic review.

Computer methods and programs in biomedicine
BACKGROUND: The application of machine learning algorithms for assessing the auditory brainstem response has gained interest over recent years with a considerable number of publications in the literature. In this systematic review, we explore how mac...

Implementation of Machine Learning to Predict Cost of Care Associated with Ambulatory Single-Level Lumbar Decompression.

World neurosurgery
BACKGROUND: With the emergence of the concept of value-based care, efficient resource allocation has become an increasingly prominent factor in surgical decision-making. Validated machine learning (ML) models for cost prediction in outpatient spine s...

Effects of data quality and quantity on deep learning for protein-ligand binding affinity prediction.

Bioorganic & medicinal chemistry
Prediction of protein-ligand binding affinities is crucial for computational drug discovery. A number of deep learning approaches have been developed in recent years to improve the accuracy of such affinity prediction. While the predicting power of t...

Systematic Improvement of the Performance of Machine Learning Scoring Functions by Incorporating Features of Protein-Bound Water Molecules.

Journal of chemical information and modeling
Water molecules at the ligand-protein interfaces play crucial roles in the binding of the ligands, but the behavior of protein-bound water is largely ignored in many currently used machine learning (ML)-based scoring functions (SFs). In an attempt to...

Interpretable Deep Learning Model for Analyzing the Relationship between the Electronic Structure and Chemisorption Property.

The journal of physical chemistry letters
The use of machine learning (ML) is exploding in materials science as a result of its high predictive performance of material properties. Tremendous trainable parameters are required to build an outperforming predictive model, which makes it impossib...

Machine learning in project analytics: a data-driven framework and case study.

Scientific reports
The analytic procedures incorporated to facilitate the delivery of projects are often referred to as project analytics. Existing techniques focus on retrospective reporting and understanding the underlying relationships to make informed decisions. Al...

Topographic design in wearable MXene sensors with in-sensor machine learning for full-body avatar reconstruction.

Nature communications
Wearable strain sensors that detect joint/muscle strain changes become prevalent at human-machine interfaces for full-body motion monitoring. However, most wearable devices cannot offer customizable opportunities to match the sensor characteristics w...

Enhanced detection of threat materials by dark-field x-ray imaging combined with deep neural networks.

Nature communications
X-ray imaging has been boosted by the introduction of phase-based methods. Detail visibility is enhanced in phase contrast images, and dark-field images are sensitive to inhomogeneities on a length scale below the system's spatial resolution. Here we...

Identifying Glucose Metabolism Status in Nondiabetic Japanese Adults Using Machine Learning Model with Simple Questionnaire.

Computational and mathematical methods in medicine
We aimed to identify the glucose metabolism statuses of nondiabetic Japanese adults using a machine learning model with a questionnaire. In this cross-sectional study, Japanese adults (aged 20-64 years) from Tokyo and surrounding areas were recruited...

Rapid identification of salmonella serovars by using Raman spectroscopy and machine learning algorithm.

Talanta
A widespread and escalating public health problem worldwide is foodborne illness, and foodborne Salmonella infection is one of the most common causes of human illness.For the three most pathogenic Salmonella serotypes, Raman spectroscopy was employed...