Enhancing Precision Oncology: Artificial Intelligence in the Identification of Cancer Molecular Subtype
Keywords
Cancer, AI for Healthcare, Good Health and Well-being
Type of Proposal
Oral Presentation
Faculty
Faculty of Science, School of Computer Science
Faculty Sponsor
Dr. Ziad Kobti
Proposal
Cancer is a top cause of mortality around the globe, presenting a significant challenge to global health. Cancer is a highly complex genomic disease, with each type exhibiting unique genetic and molecular changes at different stages, necessitating the need for personalized treatment plans. However, the diversity of cancer is not limited to different types, it also varies within cancers from the same organ. Therefore, identifying the molecular subtype of cancer is a vital step in both cancer research and clinical treatments. It will play a key role in boosting the effectiveness of existing therapies and guiding the development of future treatments, with the ultimate goal of increasing patient survival rates. The objective of this research is to utilize artificial intelligence (AI) in healthcare by introducing an innovative method that employs deep learning to identify cancer molecular subtypes through the analysis of multiple large omics datasets. This method aims to identify cancer subtypes with unprecedented precision, offering a significant leap towards personalized medicine. This research aligns with the United Nations Sustainable Development Goal No. 3, Good Health and Well-being, contributing to global efforts in combating cancer and highlighting the role of technology-driven solutions in tackling major health challenges.
Enhancing Precision Oncology: Artificial Intelligence in the Identification of Cancer Molecular Subtype
Cancer is a top cause of mortality around the globe, presenting a significant challenge to global health. Cancer is a highly complex genomic disease, with each type exhibiting unique genetic and molecular changes at different stages, necessitating the need for personalized treatment plans. However, the diversity of cancer is not limited to different types, it also varies within cancers from the same organ. Therefore, identifying the molecular subtype of cancer is a vital step in both cancer research and clinical treatments. It will play a key role in boosting the effectiveness of existing therapies and guiding the development of future treatments, with the ultimate goal of increasing patient survival rates. The objective of this research is to utilize artificial intelligence (AI) in healthcare by introducing an innovative method that employs deep learning to identify cancer molecular subtypes through the analysis of multiple large omics datasets. This method aims to identify cancer subtypes with unprecedented precision, offering a significant leap towards personalized medicine. This research aligns with the United Nations Sustainable Development Goal No. 3, Good Health and Well-being, contributing to global efforts in combating cancer and highlighting the role of technology-driven solutions in tackling major health challenges.
