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

The authors compare current machine learning algorithms with a new Explainable AI algorithm that produces a human-comprehensible decision tree alongside predictions.

The authors looked at the immunogenicity of a newly developed pneumococcal conjugate vaccine compared to a previously developed one. They found the newly developed vaccine did elicit an immune response.

This study explored how green spaces, climate, traffic, and air quality (GCTA) collectively influence asthma-related emergency department visits in the U.S using machine learning models and explainable AI.

The authors looked at the impact of citrate and lactate on glioblastoma progression. Their results provide important insights for future immunotherapies aimed at treating glioblastoma.

This study explores auxin signaling in Chlorella vulgaris, a green alga with potential for sustainable biofuel and food production. Evidence from protoplast swelling experiments suggests that C. vulgaris secretes auxin and possesses auxin import proteins, highlighting previously uncharacterized signaling pathways. These findings could support more efficient cultivation and resource extraction strategies.

The authors investigated whether the physician-patient relationship affected patient perceptions and treatment adherence.

The challenge of accurately identifying shark species is crucial for biodiversity monitoring but is often hindered by time-consuming and labor-intensive manual methods. To address this, SharkNet, a CNN model based on AlexNet, achieved 93% accuracy in classifying shark species using a limited dataset of 1,400 images across 14 species. SharkNet offers a more efficient and reliable solution for marine biologists and conservationists in species identification and environmental monitoring.

The authors examine the relationships between race, racist beliefs, and perceptions of beauty across cultures and generations.

It can be challenging for the general public to understand data on medical risk. Weseley-Jones and Mordechai tackle this issue by conducting a survey to assess people's skill and comfort with understanding medical risk information in percentage and frequency formats.