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 b...

A single chromatin immunoprecipitation (ChIP) sample does not provide enough DNA for hybridization to a genomic tiling array. A commonly used technique for amplifying the DNA obtained from ChIP assays is ligation-mediated PCR (LM-PCR). However; using this amplification method, we could not identify Oct4 binding sites on genomic tiling arrays representing 1% of the human genome (ENCODE arrays). ...

The development of new methods that might or might not involve sophisticated techniques for the quality control of food products continues increasing according to consumer requirement of food safety and authenticity issues. The production of fakes is a worldwide problem that includes food products. Some examples can be found on olive oils, honeys and alcoholic beverages such as table wines and ...

We are now well into the sequencing era of genetic analysis, and methods to investigate rare variants associated with disease remain in high demand. Currently, the more common rare variant analysis methods are burden tests and variance component tests. This report introduces a burden test known as the modified replication based sum statistic and evaluates its performance, and the performance of...

The estimation of a covariance matrix from an insufficient amount of data is one of the most common problems in fields as diverse as multivariate statistics, wireless communications, signal processing, biology, learning theory and finance. In a joint work of Marzetta, Tucci and Simon, a new approach to handle singular covariance matrices was suggested. The main idea was to use dimensionality re...

This is an expanded version of Wooldridge (2003), which provided an overview of cluster-sample methods in linear models. Here I include additional details for linear models and provide, as much as possible, a parallel treatment for nonlinear models, including a summary of strategies for dealing with data sets having a small number of clusters.

BACKGROUND The use of cluster randomized trials (CRTs) is increasing, along with the variety in their design and analysis. The simplest approach for their sample size calculation is to calculate the sample size assuming individual randomization and inflate this by a design effect to account for randomization by cluster. The assumptions of a simple design effect may not always be met; alternativ...

Abstract We study the finite sample behavior of Lasso-based inference methods such as post–double Lasso and debiased Lasso. show that these can exhibit substantial omitted variable biases (OVBs) due to Lasso's not selecting relevant controls. This phenomenon occur even when coefficients are sparse size is large larger than number Therefore, relying on existing asymptotic theory be problematic i...