During the doctoral fellowship, the research activity will be pursued along the following two main lines.

Each dataset can be represented as a similarity graph among patients, enabling the exploration of relationships across various biological and clinical contexts. An innovative strategy involves clustering patients not only independently for each data type but also maximizing similarity across different data layers. This approach facilitates the capture of complex interactions among multiple biological and clinical variables, providing a more comprehensive and integrated view of individual variability. Additionally, a focused approach can be adopted by considering specific gene subsets rather than the entire genome, aiming to concentrate on relevant biological processes or specific areas of interest, thus enriching result interpretation and yielding more targeted insights.

A second intriguing approach involves examining transcriptional regulatory networks or covariance networks among transcripts to identify patient-specific networks. This can be achieved, for example, by monitoring how the covariance matrix changes when a patient is removed from the dataset, thus identifying transcriptional variables of greater relevance to the clinical phenotype. We intend to utilize this information, along with clinical data, to develop predictive models of the clinical phenotype. To this end, we will adopt the answerALS dataset (https://www.answerals.org/) as a case study, providing a rich source of omics and clinical data on patients with amyotrophic lateral sclerosis (ALS), enabling a detailed and in-depth analysis of correlations between molecular variables and clinical manifestations of the disease.