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Examining effects of E. muscae on olfactory function in D. melanogaster

Friedman et al. | Jul 08, 2021

Examining effects of <em>E. muscae</em> on olfactory function in <em>D. melanogaster</em>

In this article, the authors investigate the effects of fungus E. muscae on fruit fly behavior. More specifically, they investigate whether this fungus affects olfaction. Their findings contribute to a broader set of studies seeking to understand how host's central nervous systems can be affected by infections.

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Using explainable artificial intelligence to identify patient-specific breast cancer subtypes

Suresh et al. | Jan 12, 2024

Using explainable artificial intelligence to identify patient-specific breast cancer subtypes

Breast cancer is the most common cancer in women, with approximately 300,000 diagnosed with breast cancer in 2023. It ranks second in cancer-related deaths for women, after lung cancer with nearly 50,000 deaths. Scientists have identified important genetic mutations in genes like BRCA1 and BRCA2 that lead to the development of breast cancer, but previous studies were limited as they focused on specific populations. To overcome limitations, diverse populations and powerful statistical methods like genome-wide association studies and whole-genome sequencing are needed. Explainable artificial intelligence (XAI) can be used in oncology and breast cancer research to overcome these limitations of specificity as it can analyze datasets of diagnosed patients by providing interpretable explanations for identified patterns and predictions. This project aims to achieve technological and medicinal goals by using advanced algorithms to identify breast cancer subtypes for faster diagnoses. Multiple methods were utilized to develop an efficient algorithm. We hypothesized that an XAI approach would be best as it can assign scores to genes, specifically with a 90% success rate. To test that, we ran multiple trials utilizing XAI methods through the identification of class-specific and patient-specific key genes. We found that the study demonstrated a pipeline that combines multiple XAI techniques to identify potential biomarker genes for breast cancer with a 95% success rate.

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The Effect of Poverty on Mosquito-borne Illness Across the United States

Kar et al. | Feb 25, 2021

The Effect of Poverty on Mosquito-borne Illness Across the United States

Mosquito-borne diseases are a major issue across the world, and the objective for this project was to determine the characteristics that make some communities more susceptible to these diseases than others. The authors identified and studied characteristics that make communities susceptible to mosquito-borne diseases, including water in square miles, average temperature, population, population density, and poverty rates per county. They found that the population of a county is the best indicator of the prevalence of mosquito-borne diseases.

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