Artificial Intelligence and its current success in a variety of fields influences also considerably the field of health care. A great number of algorithms currently provide powerful solutions, assisting medical doctors in their everyday practice in particular for diagnostic purposes. The ambition of this grant proposal is to harness the latest advances and to propose novel, explainable, and computationally efficient artificial intelligence algorithms and companion biomarkers that go beyond diagnosis and address patient stratification, treatment selection, and prognosis for lung cancer that is among the most lethal and associated with low survival. The proposed project is highly interdisciplinary, as it lies at the interface between clinical aspects, such as tools for patient stratification or treatment compatibility, and technical aspects, such as machine and deep learning for explainable representation of medical data and ensemble learning for multi-omics analysis. Highly accurate and robust algorithms are expected to extract valuable information from different biomedical data that can influence both medical and technical communities towards precision medicine.