Title: Network-based methods for outcome prediction in the "sample space",
Abstract: In this thesis we present the novel semi-supervised network-based algorithm
P-Net, which is able to rank and classify patients with respect to a specific
phenotype or clinical outcome under study. The peculiar and innovative
characteristic of this method is that it builds a network of samples/patients,
where the nodes represent the samples and the edges are functional or genetic
relationships between individuals (e.g. similarity of expression profiles), to
predict the phenotype under study. In other words, it constructs the network in
the "sample space" and not in the "biomarker space" (where nodes represent
biomolecules (e.g. genes, proteins) and edges represent functional or genetic
relationships between nodes), as usual in state-of-the-art methods. To assess
the performances of P-Net, we apply it on three different publicly available
datasets from patients afflicted with a specific type of tumor: pancreatic
cancer, melanoma and ovarian cancer dataset, by using the data and following
the experimental set-up proposed in two recently published papers [Barter et
al., 2014, Winter et al., 2012]. We show that network-based methods in the
"sample space" can achieve results competitive with classical supervised
inductive systems. Moreover, the graph representation of the samples can be
easily visualized through networks and can be used to gain visual clues about
the relationships between samples, taking into account the phenotype associated
or predicted for each sample. To our knowledge this is one of the first works
that proposes graph-based algorithms working in the "sample space" of the
biomolecular profiles of the patients to predict their phenotype or outcome,
thus contributing to a novel research line in the framework of the Network
Medicine.