DIETI Contact person / Partner: Alessandro Cilardo

Abstract

Over the past two decades, genomic has paved the way for new breakthroughs in biology and medicine. The ability to effectively handle and process genomic information, e.g. for activities like analysing and aligning DNA and protein sequences as well as generating 3D models of protein structures, is by all means crucial. The activity is focused on understanding the computational implications of next-generation sequencing approaches as well as investigating developments at the algorithm and implementation level matching the opportunities of emerging high-performance computing (HPC) accelerator-based technologies.

Objective

The main objectives of the activity will include:

• studying the computational building blocks in current genomic applications;
• investigating efficient implementation approaches based on emerging accelerator-based architectures, including GPU and FPGA devices;
• demonstrating the proposed approaches on next-generation HPC platforms, including large scale HPC facilities and/or small-scale innovative prototypes.

DIETI Contact person / Partner: Paolo Maresca

Abstract

Starting from structured and structured medical data, cognitive computing approaches has the aim to derive results and correlations that can be used later with widely validated statistical strategies such as standard and network meta-analysis.  The Standard Metanalysis and the most advanced Network Metanalysis, was used in an original research conducted at the Operative Unit of Neuroncology / Rare Tumor Regional Center of the AOU Federico II

Objective

Acquire structured and unstructured data, and implement flows and components shown in the architecture above

DIETI Contact person / Partner: Antonio Pescapè

Abstract

Health-related analyses generate huge amounts of heterogeneous data. Obtaining knowledge from these “big data” is both challenging and promising for data-driven medicine, that can benefit from AI tools to infer models for such complex problems. The PhD research activities will include model design, optimization, and validation, applied to prospective and retrospective studies, with the support of the MS partner (including data sanitization operations, and resulting analysis and validation). The PhD student will spend at least six months abroad working under the guidance of one or both our foreign partners, University of Warwick (UK) and University of Saint Louis (US).

Objectives

Main goals include the development and application of innovative approaches to prospective and retrospective studies, aimed at modeling the complexity of infectious diseases. The approaches will be supported by AI and Big Data technologies.

DIETI Contact person / Partner: Giorgio Ventre

Abstract

Multidisciplinary Prostate Cancer Units are designed to facilitate cross consultation and joint decision-making based upon diagnostic evidence to patients. At present, substantial gaps exist in their ability to accurately predict outcome and to counsel patients on short-mid and long-term treatment options. We propose to derive a Quality of Life factor through artificial intelligence and Big Data analytics, collecting, and analyzing data from 3 National Institutions, to understand how a patient’s quality of life changes over time according to type of treatment received and disease progression, also taking into account comorbidities and their own progression.

Objective

Derive a Quality of Life factor through artificial intelligence and big data analytics, to create a comprehensive patient profile that will ultimately save time, consolidate knowledge, give emotional support, provide measurement of physical and mental health, and personalized treatment.