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This study used multi-omic analysis to investigate gene expression and DNA methylation patterns associated with hypertrophic cardiomyopathy (HCM). The researchers found significant downregulation of mitochondrial and energy-related pathways, highlighting mitochondrial dysfunction—particularly altered PINK1 expression—as a potential contributor and therapeutic target in HCM.

The authors studied the accuracy of two commonly used explainable AI techniques for detecting pneumonia in chest x-rays.

This study developed an effective method for extracting valuable information from social media data and employed computer-assisted human coding alongside the AI-powered tool to analyze social media posts. The findings provide a deep understanding of the major challenges that home health care workers are experiencing and offer important implications regarding how to improve home healthcare workers' well-being, and provide suggestions on optimizing the home health care service experience.

This study used machine learning models to examine which factors most influenced U.S. household energy consumption in 2020 using data from 18,496 households.

The authors looked at how the presence of difference antibiotic resistance genes would influence antibiotic resistance in Staphylococcus aureus.

The authors looked at genes and pathways that are enriched in glioblastoma multiforme.

The authors looked factors that could lead to earlier diagnosis of cardiovascular disease thereby improving patient outcomes. They found that advances in imaging and electrocardiography contribute to earlier detection of cardiovascular disease.

Impact of carbon tax and dividend on financial security

The authors looked at the ability of different deep learning models to predict the presence of lung cancer from chest CT scans. They found that a pre-trained CNN model performed better than an autoencoder model.

Default opt-ins and social proof tags effect on decision making in an e-commerce context