Title of the project to which the candidate would be incorporated:

Interpretable deep learning for inferring cellular transcriptional and metabolic changes of genetic perturbations in cancer (PERMET2CAN)

PID2024-160726OB-I00 - Call: Knowledge Generation Projects 2024

Object of the position:

The main objective of the project is to develop a new framework based on interpretable Deep Learning (DL) to jointly infer transcriptional outcomes and metabolic changes associated with genetic perturbations. The design of the DL model will be guided by a priori biological information and will take into account two simultaneous objectives. Single-cell RNA sequencing (scRNA-Seq) data from perturbation experiments will be used. The proposed framework will allow identifying key cellular responses, transcriptional and metabolic, resulting from genetic perturbations. Understanding transcriptional changes to genetic pe...