1 code implementation • 13 May 2024 • Aakash Tripathi, Asim Waqas, Yasin Yilmaz, Ghulam Rasool
To address this challenge, we introduce HoneyBee, a scalable modular framework for building multimodal oncology datasets that leverages foundational models to generate representative embeddings.
1 code implementation • 13 May 2024 • Asim Waqas, Aakash Tripathi, Sabeen Ahmed, Ashwin Mukund, Hamza Farooq, Matthew B. Schabath, Paul Stewart, Mia Naeini, Ghulam Rasool
SeNMo proved to be a mini-foundation model for multi-omics oncology data because it demonstrated robust performance, and adaptability not only across molecular data types but also on the classification task of predicting the primary cancer type of patients.
1 code implementation • 30 Sep 2023 • Aakash Tripathi, Asim Waqas, Kavya Venkatesan, Yasin Yilmaz, Ghulam Rasool
The advancements in data acquisition, storage, and processing techniques have resulted in the rapid growth of heterogeneous medical data.
no code implementations • 11 Mar 2023 • Asim Waqas, Aakash Tripathi, Ravi P. Ramachandran, Paul Stewart, Ghulam Rasool
Recent deep learning frameworks such as Graph Neural Networks (GNNs) and Transformers have shown remarkable success in multimodal learning.
1 code implementation • 30 Jun 2021 • Asim Waqas, Ghulam Rasool, Hamza Farooq, Nidhal C. Bouaynaya
The architectures of deep artificial neural networks (DANNs) are routinely studied to improve their predictive performance.