Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations.
AggMapNet: enhanced and explainable low-sample omics deep learning with feature-aggregated multi-channel networks.
Enhanced metagenomic deep learning for disease prediction and consistent signature recognition by restructured microbiome 2D representations.
Toward ordered -omics data science: Researchers on the magic of turning metagenomic chaos into image-like patterns.
Deep learning of 2D-Restructured gene expression representations for improved low-sample therapeutic response prediction.
Robust Automated Harmonization of Heterogeneous Data Through Ensemble Machine Learning: Algorithm Development and Validation Study.
