Expression-based machine learning models for predicting plant tissue identity
S. Palande, J. Arsenault, P. Basurto-Lozada, A. Bleich, B. N. I. Brown, S. F. Buysse, N. A. Connors, S. Das Adhikari, K. C. Dobson, F. X. Guerra-Castillo, M. F. Guerrero-Carrillo, S. Harlow, H. Herrera-Orozco, A. T. Hightower, P. Izquierdo, , Krishnan
The paper uses Arabidopsis gene expression data to train machine learning models for predicting tissue identity in other flowering plants, finding that gene expression signatures are more predictive than marker genes.
bioRxiv · Aug 20, 2023Plant Biology