Browse Articles

The authors assessed the correlation between the stock, commodity, and consumer markets with the housing market.

This manuscript explores the performance of five different machine learning models in classifying brain tumors from a dataset of MRI scans. The authors find that several of the models showed >90% accuracy. Thus, the authors suggest that machine learning models demonstrate potential for effective implementation in clinical settings, including as a diagnostic tool that can be used to complement the expertise of neuroradiologists.

This study analyzes genetic alterations and expression patterns of protein kinases involved in phagocytosis across multiple cancers using TCGA data.

The authors looked at how vocabulary is impacted in Alzheimer's disease and whether it could be used a predictor of disease onset.

The authors looked at the ability to use audio clips to analyze the progression of Parkinson's disease.

This study explores the factors that influence coffee quality ratings using data from the Coffee Quality Institute. Through a regression model based on gradient descent, the authors aimed to predict coffee ratings (total cup points) and hypothesized that sweetness and the coffee producer would be the most influential factors.

The authors looked at the primary structure of lignin peroxidase in an attempt to identify mutations that would improve both the stability and solubility of the peroxidase protein. The goal is to engineer peroxidase enzymes that are stable to help break down polymers, such as PVC, into monomers that can be reused instead of going to landfills.

People with Type One diabetes often rely on Continuous Blood Glucose Monitors (CGMs) to track their blood glucose and manage their condition. Researchers are now working to help people with Type One diabetes more easily monitor their health by developing models that will future blood glucose levels based on CGM readings. Jalla and Ghanta tackle this issue by exploring the use of AI models to forecast blood glucose levels with CGM data.

The study explored the role of testosterone beyond its well-established effects on male sex characteristics, focusing on its association with non-communicable diseases (NCDs) like obesity and type 2 diabetes (T2D), using Mendelian randomization (MR) analysis on genomic data.

Skin cancer is a common and potentially deadly form of cancer. This study’s purpose was to develop an automated approach for early detection for skin cancer. We hypothesized that convolutional neural network-based models using transfer learning could accurately differentiate between benign and malignant moles using natural images of human skin.