MRI radiomics in the prediction of the volumetric response in meningiomas after gamma knife radiosurgery.

Purpose: This report presents the first investigation of the radiomics value in predicting the meningioma volumetric response to gamma knife radiosurgery (GKRS).

Methods: The retrospective study included 93 meningioma patients imaged by three Tesla MRI. Tumor morphology was quantified by calculating 337 shape, first- and second-order radiomic features from MRI obtained before GKRS.

Statistical and Machine Learning Link Selection Methods for Brain Functional Networks: Review and Comparison.

Network-based representations have introduced a revolution in neuroscience, expanding the understanding of the brain from the activity of individual regions to the interactions between them. This augmented network view comes at the cost of high dimensionality, which hinders both our capacity of deciphering the main mechanisms behind pathologies, and the significance of any statistical and/or machine learning task used in processing this data. A link selection method, allowing to remove irrelevant connections in a given scenario, is an obvious solution that provides improved utilization of these network representations.

Is there an association between paternal age and aneuploidy? Evidence from young donor oocyte-derived embryos: a systematic review and individual patient data meta-analysis.

Background: Delayed parenthood, by both women and men, has become more common in developed countries. The adverse effect of advanced maternal age on embryo aneuploidy and reproductive outcomes is well known. However, whether there is an association between paternal age (PA) and embryonic chromosomal aberrations remains controversial.

SHOPIN: Semantic Homogeneity Optimization in Protein Interaction Networks.

Protein interaction networks (PINs) are argued to be the richest source of hidden knowledge of the intrinsic physical and/or functional meanings of the involved proteins. We propose a novel method for computational protein function prediction based on semantic homogeneity optimization in PIN (SHOPIN). The SHOPIN method creates graph representations of the PIN augmented by inclusion of the semantics of the proteins and their interacting contexts.

Exploring function prediction in protein interaction networks via clustering methods.

Complex networks have recently become the focus of research in many fields. Their structure reveals crucial information for the nodes, how they connect and share information. In our work we analyze protein interaction networks as complex networks for their functional modular structure and later use that information in the functional annotation of proteins within the network.