AIMC Topic: Machine Learning

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Machine learning for predicting chronic diseases: a systematic review.

Public health
OBJECTIVES: We aimed to review the literature regarding the use of machine learning to predict chronic diseases.

Structure-Aware Multimodal Deep Learning for Drug-Protein Interaction Prediction.

Journal of chemical information and modeling
Identifying drug-protein interactions (DPIs) is crucial in drug discovery, and a number of machine learning methods have been developed to predict DPIs. Existing methods usually use unrealistic data sets with hidden bias, which will limit the accurac...

Anticipating antibiotic resistance.

Science (New York, N.Y.)
Machine learning can use clinical history to lower the risk of infection recurrence.

Augmentation of Transcriptomic Data for Improved Classification of Patients with Respiratory Diseases of Viral Origin.

International journal of molecular sciences
To better understand the molecular basis of respiratory diseases of viral origin, high-throughput gene-expression data are frequently taken by means of DNA microarray or RNA-seq technology. Such data can also be useful to classify infected individual...

Cereal grain 3D point cloud analysis method for shape extraction and filled/unfilled grain identification based on structured light imaging.

Scientific reports
Cereals are the main food for mankind. The grain shape extraction and filled/unfilled grain recognition are meaningful for crop breeding and genetic analysis. The conventional measuring method is mainly manual, which is inefficient, labor-intensive a...

Machine learning detects altered spatial navigation features in outdoor behaviour of Alzheimer's disease patients.

Scientific reports
Impairment of navigation is one of the earliest symptoms of Alzheimer's disease (AD), but to date studies have involved proxy tests of navigation rather than studies of real life behaviour. Here we use GPS tracking to measure ecological outdoor behav...

A machine learning approach-based array sensor for rapidly predicting the mechanisms of action of antibacterial compounds.

Nanoscale
Rapid and accurate identification of the mechanisms of action (MoAs) of antibacterial compounds remains a challenge for the development of antibacterial compounds. Computational inference methods for determining the MoAs of antibacterial compounds ha...

Prediction and Elucidation of Triglycerides Levels Using a Machine Learning and Linear Fuzzy Modelling Approach.

BioMed research international
INTRODUCTION: Triglycerides are lipids composed of fatty acids that provide energy to the cell. These compounds are delivered to the body's cells via lipoproteins found in the bloodstream. Increased blood triglyceride levels have been associated with...

Enhanced Watershed Segmentation Algorithm-Based Modified ResNet50 Model for Brain Tumor Detection.

BioMed research international
This work delivers a novel technique to detect brain tumor with the help of enhanced watershed modeling integrated with a modified ResNet50 architecture. It also involves stochastic approaches to help in developing enhanced watershed modeling. Cancer...

Machine Learning-Based MRI LAVA Dynamic Enhanced Scanning for the Diagnosis of Hilar Lesions.

Computational and mathematical methods in medicine
OBJECTIVE: To explore the value of machine learning-based magnetic resonance imaging (MRI) liver acceleration volume acquisition (LAVA) dynamic enhanced scanning for diagnosing hilar lesions.