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Alfredo Ferro's impact on information technology has traversed diverse domains, encompassing Computational Logic, Data Mining, Bioinformatics, and Complex Systems. After first studying Mathematics at the University of Catania, he received a Ph.D. in Computer Science from NYU in 1981, working under the supervision of Jacob Theodor (Jack) Schwartz. He returned to the University of Catania where he established the Computer Science undergraduate program, served as the coordinator of the Ph.D. program in Computer Science, cofounded the Ph.D. program in Biology, Human Genetics, and Bioinformatics, and retired as a full professor in 2021.
Alfredo's academic career as a computer scientist is characterized by two distinct research phases: Computational Logic until approximately 1995, followed by a notable focus on Data Mining and Bioinformatics. The contributions in this volume reflect the quality and the scope of his personal and collaborative successes.He also taught andinspired many excellent scientists. A pioneering initiative was to establish summer schools for Ph.D. students in 1989, leading to the so-called Lipari School, now the J.T. Schwartz International School for Scientific Research, where Alfredo continues to serve as director. This prestigious series includes schools focused on Computer Science, Complex Systems, and Computational Biology, featuring world-class scientists as lecturers and mentors.
Inhaltsverzeichnis
Computational Logic.- The Early Development of SETL.- Onset and Today's Perspectives of Multilevel Syllogistic.- An Automatically Verified Prototype of a Landing Gear System.- A Sound and Complete Validity Test for Formulas in Extensional Multi-Level Syllogistic.- Computational Biology and Complex Systems.- Advances in Network-Based Drug Repositioning.- Integer Programming Based Algorithms for Overlapping Correlation Clustering.- Deep Learning Models for LC-MS Untargeted Metabolomics Data Analysis.- The Search for Cancer Drivers: Basic Principles and Computational Approaches.- Inferring a Gene Regulatory Network from Gene Expression Data. AnOverview of Best Methods and a Reverse Engineering Approach.- Efficient Random Strategies for Taming Complex Socio-Economic Systems.- Critical Density for Network Reconstruction.- Motif Finding Algorithms: a Performance Comparison.
