Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data

no code implementations • NeurIPS 2018 • Ehsan Hajiramezanali, Siamak Zamani Dadaneh, Alireza Karbalayghareh, Mingyuan Zhou, Xiaoning Qian

Second, compared to the number of involved molecules and system complexity, the number of available samples for studying complex disease, such as cancer, is often limited, especially considering disease heterogeneity.

Multi-Task Learning

Optimal Bayesian Transfer Learning

no code implementations • 2 Jan 2018 • Alireza Karbalayghareh, Xiaoning Qian, Edward R. Dougherty

Transfer learning has recently attracted significant research attention, as it simultaneously learns from different source domains, which have plenty of labeled data, and transfers the relevant knowledge to the target domain with limited labeled data to improve the prediction performance.

Domain Adaptation Transfer Learning