Błażej Miasojedow

DiscrimAlign learns substitution matrices and gap penalties for sequence alignment to predict microRNA-target interactions, achieving performance comparable to neural networks while being more interpretable.

CONET infers tumor evolutionary trees and copy number calls from single-cell data using a probabilistic model with MCMC and regularization.

The paper introduces a linear deconvolution method using optimal transport to improve accuracy in mass spectrometry by reducing sensitivity to measurement inaccuracies.