Learning with Kernels on Graphs: DAG-based kernels, data streams and RNA function prediction.

In many application domains data can be naturally represented as graphs. When the application of analytical solutions for a given problem is unfeasible, machine learning techniques could be a viable way to solve the problem. Classical machine learning techniques are defined for data represented in a vectorial form. Recently some of them have been extended to deal directly with structured data. Among those techniques, kernel methods have shown promising results both from the computational complexity and the predictive performance point of view. Kernel methods allow to avoid an explicit mapping in a vectorial form relying on kernel functions, which informally are functions calculating a similarity measure between two entities. However, the definition of good kernels for graphs is a challenging problem because of the difficulty to find a good tradeoff between computational complexity and expressiveness. Another problem we face is learning on data streams, where a potentially unbounded sequence of data is generated by some sources. There are three main contributions in this thesis. The first contribution is the definition of a new family of kernels for graphs based on Directed Acyclic Graphs (DAGs). We analyzed two kernels from this family, achieving state-of-the-art results from both the computational and the classification point of view on real-world datasets. The second contribution consists in making the application of learning algorithms for streams of graphs feasible. Moreover,we defined a principled way for the memory management. The third contribution is the application of machine learning techniques for structured data to non-coding RNA function prediction. In this setting, the secondary structure is thought to carry relevant information. However, existing methods considering the secondary structure have prohibitively high computational complexity. We propose to apply kernel methods on this domain, obtaining state-of-the-art results.

Mapping new non-genetic dependencies in malignant pleural mesothelioma

Malignant Pleural Mesothelioma (MPM) is a very aggressive cancer whose incidence is growing worldwide. MPM escapes the classical models of carcinogenesis and lacks a distinctive genetic fingerprint, keeping obscure the molecular events that lead to tumorigenesis. This severely impacts on the limited therapeutic options and on the lack of specific biomarkers, concurring to make MPM one of the deadliest cancers. Here we combined a functional genome-wide loss of function CRISPR/Cas9 screening with patients’ transcriptomic and clinical data, to identify genes essential for MPM progression. Besides, we explored the role of non-coding RNAs to MPM progression by analysing gene expression profiles and clinical data from the MESO-TCGA dataset. We identified TRIM28 and the lncRNA LINC00941 as new vulnerabilities of MPM, associated with disease aggressiveness and bad outcome of patients. TRIM28 is a multi-domain protein involved in many processes, including transcription regulation. We showed that TRIM28 silencing impairs MPM cells’ growth and clonogenicity by blocking cells in mitosis. RNA-seq profiling showed that TRIM28 loss abolished the expression of major mitotic players. Our data suggest that TRIM28 is part of the B-MYB/FOXM1-MuvB complex that specifically drives the activation of mitotic genes, keeping the time of mitosis. In parallel, we found LINC00941 as strongly associated with reduced survival probability in MPM patients. LINC00941 KD profoundly reduced MPM cells’ growth, migration and invasion. This is accompanied by changes in morphology, cytoskeleton organization and cell-cell adhesion properties. RNA-seq profiling showed that LINC00941 KD impacts crucial functions of MPM, including HIF1α signalling. Collectively these data provided new insights into MPM biology and demonstrated that the integration of functional screening with patients’ clinical data is a powerful tool to highlight new non-genetic cancer dependencies that associate to a bad outcome in vivo, paving the way to new MPM-oriented targeted strategies and prognostic tools to improve patients risk-based stratification.