Mike Phuycharoen

Second ‘doomsday’ oarfish washes up on California beach in three months Kelp, that can’t be good. A rare, massive fish known as the harbinger of doom has washed up on a California shore — for the second time in just three …

Abstract We introduce the Unbiasing Variational Autoencoder (UVAE), a novel computational framework developed for the integration of unpaired biomedical data streams, with a particular focus on clinical flow cytometry. UVAE effectively addresses the challenges of batch effect correction and data alignment by training a semi-supervised model on partially labeled datasets. This approach enables the simultaneous normalisation and integration of diverse data within a shared latent space.

Abstract Understanding genetic variants effects on the epigenome is crucial for interpreting genome-wide association studies (GWAS) results, yet profiling these effects across the non-coding genome remains challenging due to the scalability limits of experimental methods. This necessitates accurate computational models. Existing machine learning approaches, while progressively improving, are confined to the cell types they were trained on, limiting their applicability.