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TL-iGeneCombo: Transfer Learning for Individual Sample Specific Gene Combination Effect Prediction and Selection in Cancer Cells
TS-075184 — Problem Synthetic lethality (SL) is defined as a gene combination effect (GCE) of two genes where loss-of-function of both genes, rather than one, causes cell death. While carrying strong therapeutic potential in the treatment of cancer, SL interactions are context specific and sparse, thereby limi…
  • College: College of Medicine (COM)
  • Inventors: Li, Lang; Gökbağ, Birkan
  • Licensing Officer: Hampton, Andrew

A novel machine learning model for prediction of ICI responsive patients based on ICI pharmacokinetics
TS-075183 — Problem Current eligibility biomarkers for Immune checkpoint inhibitors (ICIs) poorly predict patient’s response. Drug clearance, both at baseline and over time, is a stronger predictor of ICI effectiveness, however the current non-linear mixed effects modeling (NLME) approach for estimating …
  • College: College of Pharmacy
  • Inventors: Phelps, Mitchell "Mitch"; Adeluola, Adeoluwa; Coss, Christopher; Kim, Kyeongmin; Mo, Xiaokui "Molly"; Owen, Dwight
  • Licensing Officer: Hampton, Andrew

EmitGCL: Prediction of Future Metastasis based on Graph Contrastive Learning
TS-075168 — Problem Metastasis is the primary driver of cancer-related deaths, yet accurately predicting its onset is a major clinical challenge. Current diagnostic tools often fail to detect occult metastatic cells and metastatic precursor cells, resulting in either overdiagnosis, with unnecessary treatments,…
  • College: College of Medicine (COM)
  • Inventors: Ma, Qin; Wang, Xiaoying
  • Licensing Officer: Hampton, Andrew

A Regularized Conditional GAN for Posterior Sampling in Inverse Problems
TS-063238 — A novel regularization technique applicable for medical imaging applications that leverages conditional generative adversarial networks (cGANs) to generate reconstructed images in significantly shorter timeframes. The Need Several techniques are used for image reconstruction in the medical aren…
  • College: College of Engineering (COE)
  • Inventors: Bendel, Matthew; Ahmad, Rizwan; Schniter, Philip "Phil"
  • Licensing Officer: Hampton, Andrew

Hybrid Collaborative Filtering Methods for Recommending Search Terms to Clinicians
TS-063237 — Electronic Health Records (EHR) are used in over 88% of all US medical clinics to improve care and streamline data. In addition, they enable sharing of data to multiple providers dealing with the same patient, thereby enhancing efficiency and care. The Need In the last decade, medical practices…
  • College: College of Medicine (COM)
  • Inventors: Ning, Xia; Peng, Bo; Ren, Zhiyun
  • Licensing Officer: Hampton, Andrew

Patient Access Prioritization Score
TS-063212 — When a patient demonstrates signs of imminent clinical deterioration, it is essential to immediately assess and treat the patient to prevent intensive care unit transfer, cardiac arrest, or death. These tasks often include transferring the patient to other facilities with optimal capabilities. T…
  • College: College of Medicine (COM)
  • Inventors: Reinbolt, Raquel; Cohn, David
  • Licensing Officer: Hampton, Andrew

Convolutional Neural Network to Assess Phayngeal and Laryngeal Pathology and Function on Nasopharyngolaryngoscopy
TS-063154 — Worldwide, 686,000 new head and neck (H&N) cancers are diagnosed yearly, and 375,000 people will die annually. Human papillomavirus (HPV) is responsible for an increasing subset of H&N malignancies called oropharyngeal squamous cell carcinomas (OPSCC). Although it has a better prognosis than…
  • College: College of Engineering (COE)
  • Inventors: Krening, Samantha; Gifford, Ryan; Jhawar, Sachin; VanKoevering, Kyle
  • Licensing Officer: Hampton, Andrew

An AI Solution for Mobile Stroke Detection for TeleMedicine, Hospital and Paramedic Use
TS-060782 — Problem Statement: Diagnosis of arterial occlusion and an associated stroke can be lengthy, delaying patient access to treatment. As large vessel occlusions account for 24 to 46% of acute ischemic strokes and may require treatment in a comprehensive stroke center, early diagnosis is key. Solution:…
  • College: College of Engineering (COE)
  • Inventors: Yilmaz, Alper; Gulati, Deepak Kumar
  • Licensing Officer: Hampton, Andrew

At-Home Digital Platform for Scoring Skin Disease
TS-038429 — An automated image analysis system for the recognition and quantification of skin disease
Acne and rosacea are skin diseases that affect around 85% of individuals, the former being the most common skin condition afflicting up to 50 million people. There is no gold standard for evaluation of these skin diseases, and their treatment efficacy is generally determined according to a poorly …
  • College: College of Medicine (COM)
  • Inventors: Kaffenberger, Benjamin; Gurcan, Metin
  • Licensing Officer: Hampton, Andrew

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