SeNMo: A Self-Normalizing Deep Learning Model for Enhanced Multi-Omics Data Analysis in Oncology

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.

Predicting Patient Outcomes

Exploring Robust Architectures for Deep Artificial Neural Networks

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.

Image Classification Neural Architecture Search +1

Fitting IVIM with Variable Projection and Simplicial Optimization

no code implementations • 27 Sep 2019 • Shreyas Fadnavis, Hamza Farooq, Maryam Afzali, Christoph Lenglet, Tryphon Georgiou, Hu Cheng, Sharlene Newman, Shahnawaz Ahmed, Rafael Neto Henriques, Eric Peterson, Serge Koudoro, Ariel Rokem, Eleftherios Garyfallidis

Fitting multi-exponential models to Diffusion MRI (dMRI) data has always been challenging due to various underlying complexities.