Machine learning predicts immunoglobulin light chain toxicity through somatic mutations
Maura Garofalo, Luca Piccoli, Margherita Romeo, Maria Monica Barzago, Sara Ravasio, Mathilde Foglierini, Milos Matkovic, Jacopo Sgrignani, Raoul De Gasparo, Marco Prunotto, Luca Varani, Luisa Diomede, Olivier Alain Michielin, Antonio Lanzavecchia, Andrea Cavalli
This paper introduces LICTOR, a machine learning model that predicts immunoglobulin light chain toxicity in AL using somatic mutations, achieving 83% accuracy in predicting clinical phenotypes.
bioRxiv · Dec 15, 2020 · PreprintBioinformatics