The main objective of this PhD Thesis is the development of deep learning (DL)-based approaches to tackle inherently complex predictive problems in the domain of precision medicine. We have focused on two of the most important data modalities in personalized medicine nowadays: omics data and clinical narratives. For both, we have proposed and developed several strategies that have proven successful in overcoming the challenges encountered by DL methods when applied to clinical data.
Transfer learning (TL) has emerged as a pivotal methodology in this Thesis. TL-based approaches have been developed to counterbalance the scarcity of annotated samples, crucial for training sophisticated DL algorithms on omics data and clinical notes. For instance, TL has been leveraged in proteomics to predict methionine oxidation sites utilizing extensive phosphorylation data. Moreover, in transcriptomics, TL methodologies have facilitated effective survival prediction on a specific cancer type using a large dataset of gene expression samples from thirty other tumor types. TL strategies have also played a significant role in adapting Transformer-based models to the particularities of clinical narratives. The resulting models have being assessed on multiple tasks in the clinical Natural Language Processing (NLP) domain. Across all predictive problems examined in this Thesis, the developed domain-specific transformers have established new state-of-the-art (SOTA) performances.
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