Alexandre Allauzen

DORA predicts dose-dependent changes in transcriptomes and cell viability using a deep learning autoencoder, outperforming existing methods in viability prediction.

This paper explores active learning strategies for synergistic drug discovery, finding that environmental features improve predictions and that small batch sizes with dynamic strategies can identify 60% of synergistic pairs from 10% of the combinatorial space.

This paper introduces D-LIM, an interpretable neural network for genotype-to-fitness mapping, achieving state-of-the-art accuracy in predicting gene and gene-environment interactions in mutational scanning data.