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

The authors looked at genes that were differentially expressed in patients who had and had not had febrile seizures to determine what differences in gene expression existed.

The authors combine fine needle aspiration biopsy and machine learning algorithms to develop a breast cancer detection method suitable for resource-constrained regions that lack access to mammograms.

The authors looked at different factors, such as age, pre-existing conditions, and geographic region, and their ability to predict what an individual's health insurance premium would be.

The authors computationally screened potential ALDH1 inhibitors, for use as potential cancer therapeutics.

The authors used machine learning to predict the presence of anxiety and burnout in students based on facial expressions and demographic information.

The authors used Monte Carlo simulations to assess the impacts of various factors on neonatal seizure risk.

This paper investigates the potential anticancer properties of Uvularia perfoliata by testing its effects on the viability of uveal melanoma cells.

The authors looked at different ways to build computational resources that would analyze shooting form for basketball players.

This study used an improved CMS-seq method to profile 5hmC in ormalin-fixed and paraffin-embedded (FFPE) samples from HNC tumors and adjacent normal tissues, identifying three genes (PRKD2, HADHA, and AIPL1) with promising potential as biomarkers for Head and neck cancer (HNC) diagnosis.