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The authors look at using publicly available data and machine learning to see if they can develop a criminal activity index for counties within the state of California.

A common form of Acne is caused by a species of bacterium called Cutibacterium acnes. By using a predictive algorithm and structural analysis, the authors identified 5 small molecules with high affinity to growth factors in Catibacterium acnes. This has potential implications for supplemental skincare products.

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.

In this study, the authors test whether providing a larger dataset of glass transition temperatures (T_g) to train the machine-learning platform Polymer Genome would improve its accuracy. Polymer Genome is a machine learning based data-driven informatics platform for polymer property prediction and T_g is one property needed to design new polymers in silico. They found that training the model with their larger, curated dataset improved the algorithm's T_g, providing valuable improvements to this useful platform.

The authors test the effectiveness of machine learning to predict onset of cardiovascular disease.

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.

Using machine learning to predict the risk of algae bloom

Pulmonary diseases like lung cancer and valley fever pose serious health challenges, making accurate and rapid diagnostics essential. This study developed a MATLAB-based software tool that uses computer vision techniques to differentiate between these diseases by analyzing features of lung nodules in CT scans, achieving higher precision than traditional methods.

The authors develop and test a machine learning algorithm for predicting diabetes diagnoses.

The authors looked at using genetic algorithms to look at the Indian labor market and what features might best explain any variation seen. They found that features such as economic growth and household consumption, among others, best explained variation.