This research develops an affordable, rapid genetic testing system to personalize antidepressant treatment. By detecting DNA mutations that affect drug metabolism, the technology helps doctors prescribe the right medication for each patient. The goal is to reduce ineffective treatments and improve mental health care—especially for veterans struggling with PTSD and depression.

Cancer often becomes resistant to treatment due to the protein CDK8, which helps reprogram cancer cells. Traditional inhibitors fail because CDK8 still acts as a structural scaffold. This research develops targeted degraders that use the cell’s recycling system to eliminate CDK8 entirely, preventing resistance and improving future cancer therapies.

This research applies machine learning to genetic data to distinguish harmless DNA variations from cancer-causing mutations. By treating DNA like a crime scene, the model learns to identify which genetic changes truly drive breast cancer risk, supporting more accurate diagnosis and informed clinical decision-making.

This research uses artificial intelligence to support treatment decisions for rare diseases. By organizing verified medical knowledge into an AI assistant, it helps clinicians and families access reliable guidance, reducing the treatment odyssey and transforming rare-disease diagnoses into clearer, more hopeful care pathways.

This research develops DNA-origami-enhanced nanopores to detect individual biomolecules from a single drop of blood. By slowing molecules and reading their electrical signatures with machine learning, the technology enables rapid, ultra-early disease diagnosis without traditional laboratory testing.

This research targets cancer more precisely by focusing on a unique region of the PLK1 protein that drives tumor growth. By designing drugs that bind specifically to this domain using AI and laboratory testing, the approach aims to kill cancer cells while sparing healthy tissue.

Chickenpox is usually harmless, yet the same virus can cause severe brain infections in some individuals. This research shows that a genetic variant in an immune-system gene reduces antiviral defense, allowing greater viral replication. Identifying such variants helps explain individual vulnerability to severe viral disease.

Genetic cardiomyopathies arise from DNA errors that disrupt vital heart proteins and can be fatal in childhood. This research improves heart-targeted gene therapy by guiding treatments through the bloodstream using chemokine “traffic signals” and avoiding immune interference, enabling therapies to reach the heart more efficiently and potentially cure inherited heart disease.

This research uses immune cell “molecular fingerprints” to rapidly detect cancer from a single drop of blood. By combining nanosensors and machine learning, subtle changes in B cells can be identified within minutes. The approach offers fast, accurate, and low-cost cancer detection with the potential to significantly improve early diagnosis and survival.

Antibiotic resistance threatens to return medicine to a pre-antibiotic era. This research uses machine learning to study how bacteria balance resistance to antibiotics and bacteriophages. By revealing genetic trade-offs between attack and defense, the work enables smarter combination therapies that exploit bacterial weaknesses and prevent otherwise deadly infections.