Pietro Hiram Guzzi

Parallel processing of genomics data

The availability of high-throughput experimental platforms for the analysis of biological samples, such as mass spectrometry, microarrays and Next Generation Sequencing, have made possible to analyze a whole genome in a single experiment. Such platforms produce an enormous volume of data per single experiment, thus the analysis of this enormous flow of data poses several challenges in term of data storage, preprocessing, and analysis. To face those issues, efficient, possibly parallel, bioinformatics software needs to be used to preprocess and analyze data, for instance to highlight genetic variation associated with complex diseases. In this paper we present a parallel algorithm for the parallel preprocessing and statistical analysis of genomics data, able to face high dimension of data and resulting in good response time. The proposed system is able to find statistically significant biological markers able to discriminate classes of patients that respond to drugs in different ways. Experiments performed ...

LiSE: A Personal Booklet for Health Care Annotation

Citizens empowerment in managing their own health process leads to increased demand in ICT services. The use of data management and communication techniques to manage information in health systems can provide benefits. Health information systems contain information to ease care access, reduce costs and make health care more efficient. In this context, we focus on the development of a framework to allow citizens in managing and sharing health related data with clinicians and with family doctors. In fact, citizens can play a crucial role in their own health care. Electronic Clinical Records (ECR) contain all information regarding their own clinical related data which can hence be made available for sharing and management. This work presents LiSE (for Libretto Sanitario Elettronico, health personalized booklet), a system implemented for the Smart Health 2.0 Project, as an instrument of citizen/patient empowerment for managing his own care process and for communicating information with family doctors. LiSE is a health information system for clinical data information management, annotated by citizens, overcoming the lack of communication protocols in the other systems currently in use. We report the LiSE structure, we sketch the practical clinical applications and we report user experiences.

Database Community and Health Related Data: Experiences Through the Last Decade

Database community has been involved in topics related to improve data-related techniques or to solve data access efficiency. Health domain has been attracting the interest of database community as an application domain for many database research topics, including: (i) health data heterogeneity (e.g., different health bioimages protocols), (ii) data size (e.g., patient health related data), (iii) biomedical signals (e.g., electrocardiography data, ECG), (iv) geographical data (e.g., epidemiological one), and more recently (v) genomic and proteomic data as well as NGS data. In this chapter we present experiences from the last decade, made in a medical school, where we used database experiences to manage and analyse clinical, biological and health related data. The methodology is problem oriented and shows how to start from a problem defined in the medical domain and choose and apply techniques often known by the database community. In this chapter interesting results, in terms of applications to the clinical and medical domains, are reported.

Educational Module on Genomic Sequence Alignment Using HPC

Aligning Sequences-Sequentially and Concurrently,” an educational computational science module by the authors and available online, develops a sequential algorithm to determine the highest similarity score and the alignments that yield this score for two DNA sequences. Moreover, the module considers several approaches to parallelization and speedup. Besides a serial implementation in C, a parallel program in C/MPI is available. This paper describes the module and details experiences using the material in a bioinformatics course at University “Magna Græcia” of Catanzaro, Italy. Besides being appropriate for such a course, the module can provide a meaningful application for a high performance computing or a data structures class. CCS Concepts • Social and professional topics~Computing education • Theory of computation~Parallel algorithms • Theory of computation~Dynamic programming • Applied computing~Bioinformatics

A New ICT Based Model for Wellness and Health Care

Health promotion represents the principal process to empowerment the citizens. The health models focus on helping people to prevent illnesses through their behavior, and on looking at ways in which a person can pursue better health. Information and Communications Technologies (ICTs) may play an important role in the definition and use of these health models; ICT solutions allow exchange of information between health professionals, communities, producers of health research and other actors health. Starting from the current models, we propose a new model that identifies the principle actions to improve behavior and health of the citizens, highlighting specific aspects of daily life and proponing ICT solutions to allow the implementation of these actions.

A system for geoanalysis of clinical and geographical data

Patients enrolled in clinical trials are regularly subject to biological analyses and related data is included in Electronic Medical Records (EMRs) to summarize patient health status and to support administrative information. Well defined protocols guide the bioanalytes studies on patients. Often EMRs also contain geographical data about patients, i.e. place of birth and place of living. The integration of geographical data and biological analytes may represent a meaningful way to extract hidden information from data. For instance, possible correlations among outlier patients and some feature of areas they live in. In collaboration with the University Hospital of Catanzaro, we designed a framework able to integrate and analyze biological analytes. The system is able to relate biological data to diagnosis codes and to analyze integrated data against geographic areas of interest. The aim is to show correlations among patients features (e.g. cluster of patients with similar profiles or outlier patients) and areas features (e.g. presence of power grids or polluted sites). In addition we present a study on correlations between cardiovascular diseases and water quality in Calabria.

Technologies for Discovering Protein Interactions

This chapter contains sections titled: Introduction Techniques Investigating Physical Interactions Technologies Investigating Kinetic Dynamics Summary

Algorithms Comparing Protein Interaction Networks

This chapter contains sections titled: Introduction Local Alignment Algorithms Global Alignment Algorithms Summary

Protein-To-Protein Interaction DATABASES

This chapter contains sections titled: Introduction Databases of Experimentally Determined Interactions Databases of Predicted Interactions Metadatabases: Integration of PPI Databases Summary

Ontology-Based Analysis of Protein Interaction Networks

This chapter contains sections titled: Definition of Ontology Languages for Modeling Ontologies Biomedical Ontologies Ontology-Based Analysis of Protein Interaction Data Semantic Similarity Measures of Proteins The Gene Ontology Annotation Database (GOA) FussiMeg and ProteinOn Summary