Ricerc@Sapienza

Frataxin (FXN) is a highly conserved protein found in prokaryotes and eukaryotes that is required for efficient regulation of cellular iron homeostasis. Experimental evidence associates amino acid substitutions of the FXN to Friedreich Ataxia, a neurodegenerative disorder. Recently, new thermodynamic experiments have been performed to study the impact of somatic variations identified in cancer tissues on protein stability.

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images
of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL
maps are derived through computational staining using a convolutional neural network trained to
classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and

Precision oncology uses genomic evidence to match patients with treatment but often fails to
identify all patients who may respond. The transcriptome of these “hidden responders” may reveal
responsive molecular states. We describe and evaluate a machine-learning approach to classify
aberrant pathway activity in tumors, which may aid in hidden responder identification. The
algorithm integrates RNA-seq, copy number, and mutations from 33 different cancer types across
The Cancer Genome Atlas (TCGA) PanCanAtlas project to predict aberrant molecular states in

Recently, a daily routine for associative neural networks has been proposed: the network Hebbian-learns during the awake state (thus behaving as a standard Hopfield model), then, during its sleep state, it consolidates pure patterns and removes spurious ones, optimizing information storage: this forces the synaptic matrix to collapse to the projector one (ultimately approaching the Kanter-Sompolinsky model), allowing for the maximal critical capacity (for symmetric interactions).

The aim of this special issue is to provide a picture of the state-of-the-art and open challenges in machine learning from a statistical physics perspective, mainly that of disordered systems.

In this paper we develop statistical algorithms to infer possible cardiac pathologies, based on data collected from 24 h Holter recording over a sample of 2829 labelled patients; labels highlight whether a patient is suffering from cardiac pathologies. In the first part of the work we analyze statistically the heart-beat series associated to each patient and we work them out to get a coarse-grained description of heart variability in terms of 49 markers well established in the reference community.

We consider a three-layer Sejnowski machine and show that features learnt via contrastive divergence have a dual representation as patterns in a dense associative memory of order P = 4. The latter is known to be able to Hebbian store an amount of patterns scaling as NP -1, where N denotes the number of constituting binary neurons interacting P wisely.

Interpretability, accuracy and a solid neurophysiological basis can be considered as the main requirements for the classification model to monitor motor imagery tasks in post-stroke motor recovery paradigms supported by the brain-computer interface technology. This study aimed at comparing the accuracy performance of different classification approaches applied on a dataset of 15 stroke patients. We also explored how the variation in the dimensionality of the feature domain would influence the different classifier performance.

Educational Institutions data constitute the basis for several important analyses on the educational systems; however they often contain not negligible shares of missing values, for several reasons. We consider in this work the relevant case of the European Tertiary Education Register (ETER), describing the Educational Institutions of Europe. The presence of missing values prevents the full exploitation of this database, since several types of analyses that could be performed are currently impracticable.

The imputation of missing values in the detail data of Educational Institutions is a difficult task. These data contain multivariate time series, which cannot be satisfactory imputed by many existing imputation techniques. Moreover, almost all the data of an Institution are interconnected: the number of graduates is not independent from the number of students, the expenditure is not independent from the staff, etc. In other words, each imputed value has an impact on the whole set of data of the institution.