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This paper utilizes multiple publicly available cancer datasets to explore the abundance of different genetic dependencies across various cancer types. They explore this by looking at four genes highly associated with cancer and their genetic and pathogenic relationships.

Here the authors propose integrating magnetic iron (II) oxide nanoparticles into the high-density polyethylene linings of disposable paper cups to create a waterproof, magnetically responsive composite liner. Their findings demonstrate that these nanoparticles successfully bond with the plastic layer without disrupting its structural integrity, offering a viable method to improve plastic recovery through magnetic recycling and mitigate global microplastic pollution.

Autonomous drone imaging combined with machine learning offers a promising approach for early detection of invasive species. In this study, students built an autonomous drone and compared three models: CNN, SGDC, and XGBoost, to identify Brassica nigra from aerial footage. Their results show that CNNs most effectively recognize key visual features, demonstrating strong potential for supporting conservation and invasive plant management.

The authors used developed a machine learning tool for studying social media discourse surrounding use of tobacco and cannabis.

The authors looked at ways that seizures in patients with glioblastoma could be treated using C. elegans as a model system.

Determination of Penalty Kick Success in Soccer

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.

The authors develop a machine learning method to reduce misclassification of objects in safety-critical applications such as medical diagnosis.

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

Using machine learning to predict the risk of algae bloom