Haplotype assembly and viral quasispecies reconstruction are challenging tasks concerned with analysis of genomic mixtures using sequencing data.

The premise of semi-supervised learning (SSL) is that combining labeled and unlabeled data yields significantly more accurate models.

KL divergence between the trained posterior distribution obtained by infinitesimal step size gradient descent and a Gaussian prior.

However,GNNs have notconsidered different types ofuncertainties associated with class probabilities to minimize risk of increasing misclassification under uncertainty in real life. In this work, we propose a multi-source uncertainty framework using a GNN that reflects various types of predictive uncertainties in both deep learning and belief/evidence theory domains fornodeclassification predictions.