The American Association for Cancer Research (AACR) is the largest cancer research member organisation dedicated to accelerating the conquest of cancer, with more than 54,000 members across 130 countries and territories.
The AACR Annual Meeting is a distinguished gathering of the global cancer research community including clinicians, translational scientists, cancer advocates, and pharmaceutical and technology companies eager to share and discuss the most recent advancements in cancer research and treatment.
We are delighted to contribute to this important event with scientific poster and oral presentations, showcasing Concr’s consistent progress in using our cosmology-based computational modelling to maximise the value of our partners’ data through generating meaningful biological intelligence for drug development.
You are invited to visit us on Tuesday 18th April at our posters or attend our presentation in the afternoon. If you’d like to schedule a 1:1 meeting with one of our team, please get in touch at info@concr.co.
Event Details
Dates: April 14-19, 2023
Venue: Orange County Convention Center, Orlando, Florida
Concr Presentations
- Session: Clinical Applications of Artificial Intelligence and Mathematical Oncology
- Session Date and Time: Tuesday Apr 18, 2023, 2:30 PM - 4:30 PM
- Location: Room W221, Level 2, Orange County Convention Center
- Title: “A prognostic machine learning model for early breast cancer which combines clinical and genetic data in patients treated with neo/adjuvant chemotherapy”
- Session: Artificial Intelligence: From Pathomics to Radiomics
- Session Date and Time: Tuesday Apr 18, 2023, 1:30 PM - 5:00 PM
- Location: Poster Section 33 | Poster Board Number: 7
- Title: “A deep learning approach (AI) which accurately identifies breast tumor cells, tumor infiltrating lymphocytes (TILS) and fibroblasts from H&E slides”
- Session: Artificial Intelligence: From Pathomics to Radiomics
- Session Date and Time: Tuesday Apr 18, 2023, 1:30 PM - 5:00 PM
- Location: Poster Section 33 | Poster Board Number: 12
- Title: “Using machine learning to predict tissue of origin from somatic mutation features”
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