Activities

The AI4ChemoBrain project consists of three main activities:

1. ML/AI model training: historical preclinical cognitive and omics data will be used according to supervised learning techniques;
2. ML/AI model testing: the model obtained in activity 1., will be tested with a data set derived from a preclinical chemobrain model, in order to assess the predictive ability of the chemobrain (unsupervised learning techniques) in comparison to conventional and correlation statistics;
3. ML/AI model validation by:
   1. Data set derived from a second preclinical model of chemobrain in subjects with cognitive decline;
   2. Data set derived from an external cohort.

The combination of the data sets will enable to study the contribution of each descriptor with respect to the model’s predictive ability. Data augmentation, transfer learning and fine-tuning techniques may be used to overcome any problems related to the availability of large data sets in the context of the chemobrain in order to improve the predictive capabilities of the ML/AI model.