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IVF clinics use AI to rank embryos, but these systems can inherit hidden bias. We built EquiEmbryo AI to detect unfair gaps between groups and retrain models to be more fair without losing accuracy.
AI-powered lung cancer malignancy risk assessment tool combining deep learning with clinical risk factors
We built an engine that takes a patient's genetic mutation, cross-references it with the live air and soil toxicity in their exact neighborhood, and automatically generates a clinical action plan.
A modern canary in the coal mine, Canary maps climate-linked health inequity across 3,000 U.S. counties and measures how structural disadvantage amplifies risk.
We transform raw tumor mutations into an instant evolution blueprint, revealing the cancer’s origins, its branching clones, and the seeds of resistance.
Mammoly believes early detection saves lives, and everyone deserves an equal chance at it.
Our project uses genetic data to predict the likelihood of developing different types of cancer, helping turn complex gene and mutation information into meaningful risk insights.
A cost effective way to accurately assess cancer type and drugability.
A convolutional neural network trained on NCBI data to detect programmed ribosomal frameshifting sites. The model specializes in viral -1 slips, but has been trained on a variety of slippage sites.
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