JOIN US IN SHAPING THE CONNECTIONS BETWEEN CANCER RESEARCH AND AI.
Our mission is to enhance cancer treatment and knowledge by harnessing the power of computer intelligence. Guided by our vision that computers will eventually be able to think and perform similar tasks as humans, we strive to develop cutting-edge artificial intelligence and statistical approaches for building system models of tumors. This way opening the doors to collaboration with experimental labs and pharmaceutical companies . Our lab fosters a diverse, collaborative environment for advancing cancer research.
Our lab is dedicated to developing innovative therapies and identifying predictive biomarkers for late-stage cancers. For this it is crucial that we explore the following areas:
1) Factors that contribute to tumor micro-environments.
2) The potential for reversal
3) The patients who could benefit from these techniques.
The current program focuses on the development and design of new immunotherapies for advanced cancer patient, and tailor our knowledge for pediatric cancers and minority populations.
February 2024: Dr Kushal Virupakshappa gets awarded the 2024 AAI Intersect Fellowship for Computational Scientists and Immunologists: Congratulations!
December 2023: Our New H100 Machine Running: Happy holidays!
November 2023: David Arredondo Joins the Lab as Postdoctoral Trainee: David earned his degree from the UNM School of Engineering, specializing in DNA Nanotechnology and Molecular computing
November 2023: Explore TumorAI Lab's Research in Avi's Presentation at UNM Grand Rounds.
See the presentation here
October 2023: BSGP Student Clara Bertoni Joins TumorAI Lab for Rotation in Single Cell Analysis.
Our Latest Publication in Cancer Discovery
This research publication in Cancer Discovery explores the discovery of bipotent drugs as a new solution for traditional combination therapies through BipotentR to find cancer cell-specific regulators that help modulate tumor immunity and a number of other oncogenic pathways.
This tool presents relative importance to evaluating patient response to treatment and the identification of new targets for drugs that allows for efficient elimination of tumors and cancerous cells. When this approach is taken and the topmost candidate tumors are inhibited through the suppression of their metabolism, the cancer cells are more likely to be killed/destroyed.
If you are interested in some other of our publications, please refer to our page "Publications".