AIMC Topic: Machine Learning

Clear Filters Showing 22291 to 22300 of 34417 articles

A Method to Estimate Horse Speed per Stride from One IMU with a Machine Learning Method.

Sensors (Basel, Switzerland)
With the emergence of numerical sensors in sports, there is an increasing need for tools and methods to compute objective motion parameters with great accuracy. In particular, inertial measurement units are increasingly used in the clinical domain or...

Machine learning can identify newly diagnosed patients with CLL at high risk of infection.

Nature communications
Infections have become the major cause of morbidity and mortality among patients with chronic lymphocytic leukemia (CLL) due to immune dysfunction and cytotoxic CLL treatment. Yet, predictive models for infection are missing. In this work, we develop...

Improving the detection of autism spectrum disorder by combining structural and functional MRI information.

NeuroImage. Clinical
Autism Spectrum Disorder (ASD) is a brain disorder that is typically characterized by deficits in social communication and interaction, as well as restrictive and repetitive behaviors and interests. During the last years, there has been an increase i...

Prediction of peptide binding to MHC using machine learning with sequence and structure-based feature sets.

Biochimica et biophysica acta. General subjects
Selecting peptides that bind strongly to the major histocompatibility complex (MHC) for inclusion in a vaccine has therapeutic potential for infections and tumors. Machine learning models trained on sequence data exist for peptide:MHC (p:MHC) binding...

Characterizing Protein-Ligand Binding Using Atomistic Simulation and Machine Learning: Application to Drug Resistance in HIV-1 Protease.

Journal of chemical theory and computation
Over the past several decades, atomistic simulations of biomolecules, whether carried out using molecular dynamics or Monte Carlo techniques, have provided detailed insights into their function. Comparing the results of such simulations for a few clo...

Denoising arterial spin labeling perfusion MRI with deep machine learning.

Magnetic resonance imaging
PURPOSE: Arterial spin labeling (ASL) perfusion MRI is a noninvasive technique for measuring cerebral blood flow (CBF) in a quantitative manner. A technical challenge in ASL MRI is data processing because of the inherently low signal-to-noise-ratio (...

A 3D deep supervised densely network for small organs of human temporal bone segmentation in CT images.

Neural networks : the official journal of the International Neural Network Society
Computed Tomography (CT) has become an important way for examining the critical anatomical organs of the human temporal bone in the diagnosis and treatment of ear diseases. Segmentation of the critical anatomical organs is an important fundamental st...

Application of machine learning to predict monomer retention of therapeutic proteins after long term storage.

International journal of pharmaceutics
An important aspect of initial developability assessments as well formulation development and selection of therapeutic proteins is the evaluation of data obtained under accelerated stress condition, i.e. at elevated temperatures. We propose the appli...

Understanding Opioid Use Disorder (OUD) using tree-based classifiers.

Drug and alcohol dependence
BACKGROUND: Opioid Use Disorder (OUD), defined as a physical or psychological reliance on opioids, is a public health epidemic. Identifying adults likely to develop OUD can help public health officials in planning effective intervention strategies. T...

Machine Learning Classifies Core and Outer Fucosylation of N-Glycoproteins Using Mass Spectrometry.

Scientific reports
Protein glycosylation is known to be involved in biological progresses such as cell recognition, growth, differentiation, and apoptosis. Fucosylation of glycoproteins plays an important role for structural stability and function of N-linked glycoprot...