From Computational Logic to Computational Biology: Essays Dedicated to Alfredo Ferro to Celebrate His Scientific Career (Lecture Notes in Computer Science, 14070)

Preface
Grazie Alfredo!
Reviewers of This Volume
Acknowledgements
Contents
Computational Logic
The Early Development of SETL
	References
Onset and Today's Perspectives of Multilevel Syllogistic
	1 An Inference Mechanism for Membership
		1.1 The Discovery of Multi-level Syllogisic, Dubbed MLS
		1.2 `79/'80: Proof-Technology Seminar at NYU
		1.3 The Decidability of MLS in a Nutshell
	2 Early Offsprings of Multilevel Syllogistic
		2.1 ``Any New Results?''
		2.2 Enhancements of Multilevel Syllogistic in Catania
		2.3 Multilevel Syllogistic in New York Again
		2.4 A Downsizing and Broadenings of MLS
	3 Applications of Multilevel Syllogistic
		3.1 Algorithm Correctness Verification
		3.2 Program Transformation
		3.3 Declarative Set-Based Programming
		3.4 Hybrid Syllogistics
		3.5 Reasoners for Description Logics
		3.6 Proof-Verification: Yulog, AXL,…, ÆtnaNova
	4 Evolving Trends
		4.1 Boundaries of the Undecidable and of Infinitude
		4.2 Syllogizing About Individuals and Nested Sets
		4.3 The Long-Lasting Fortune of {log}
		4.4 The Long March Towards Computational Set Theory
	References
An Automatically Verified Prototype of a Landing Gear System
	1 Introduction
	2 Overview of {log}
		2.1 The Theories
		2.2 Set Terms
		2.3 Set and Relational Operators
		2.4 {log} Formulas
		2.5 Types in {log}
		2.6 Constraint Solving
	3 Using {log}
		3.1 {log} as a Programming Language
		3.2 {log} as an Automated Theorem Prover
	4 Encoding the Event-B Specification of the LGS in {log}
		4.1 Encoding Event-B Machines
		4.2 Encoding (Partial) Functions
		4.3 Encoding Action Predicates
		4.4 Encoding Quantifiers
		4.5 Encoding Types
	5 Encoding Proof Obligations in {log}
	6 Discussion and Comparison
	7 Final Remarks
	References
A Sound and Complete Validity Test for Formulas in Extensional Multi-level Syllogistic
	1 Introduction
	2 Entities: Individuals and Sets
	3 Definition of the Language
	4 Interpretations
	5 Classification of Interpretation
	6 Abbreviations, Truth-Value Assignments
	7 Models, Satisfiability, and Validity
	8 Superstructures
	9 Narrowing down the Notion of Interpretation
	10 First Simplifications of the Decision Problem for Validity
	11 Further Simplifications of the Validity Problem
	12 Decidability of the Validity Problem
	13 Completeness Theorem
	14 Soundness Theorem: 1st Version
	15 Proof of the Soundness Theorem, 1st Version
	16 Soundness Theorem: 2nd Version
	17 Getting Rid of Individuals
	18 A Language for 2-Level Syllogistic
	19 A Connection Between 2-Level Syllogistic and Multi-level Syllogistic
	A  Appendix 1
	B  Appendix 2
	References
Computational Biology and Complex Systems
Advances in Network-Based Drug Repositioning
	1 Introduction
	2 Recent Review Articles
	3 Eighteen Network-Based Results
		3.1 Taguchi et al. Tensor Decomposition
		3.2 Ruiz et al. Multiscale-Interactome
		3.3 Fiscon et al. SaveRUNNER
		3.4 Gysi et al. a Multimodal Approach
		3.5 Lucchetta et al. DrugMerge
		3.6 Zhou et al. a Network Proximity Measure
		3.7 Sadegh et al. CoVex
		3.8 Mall et al. Drug Autoencoder
		3.9 Maria et al. 2021: PHENSIM
		3.10 Ge et al. NeoDTI Applied to COVID-19
		3.11 Saberian et al. Learning the Similarity Metric
		3.12 Peyvandipour et al. DrugDiseaseNet
		3.13 Draghici et al. iPathwayGuide
		3.14 Stolfi et al. DIAMOND Applied to COVID-19
		3.15 Muratov et al. Viribus Unitis
		3.16 Cheng et al. Complementary Exposure
		3.17 Zitnik et al. Decagon
		3.18 Gordon et al. a Direct Proteomic Approach
	4 Discussion
		4.1 Data Availability
		4.2 Relationship to Clinical Trials
		4.3 Network Construction Vs Network Usage
		4.4 End-to-end and Intermediate Validations
		4.5 Efficacy vs Adverse Effects
		4.6 New and Legacy Software Tools
		4.7 AI and Network-Based Methods
		4.8 Pharmaco-Chemical Kinetics
		4.9 Precision Medicine and Biomarkers
		4.10 Drug Combinations
		4.11 Comparative Assessment of Network-Based Methods
	5 Conclusion
	References
Integer Programming Based Algorithms for Overlapping Correlation Clustering
	1 Introduction
	2 Preliminaries and Problem Definition
	3 Methods
		3.1 ILP-Based Algorithms
		3.2 Row-Based Clustering
		3.3 Column-Based Clustering
		3.4 Performance Evaluation
	4 Results
	5 Conclusions and Future Work
	References
Deep Learning Models for LC-MS Untargeted Metabolomics Data Analysis
	1 Introduction
		1.1 Metabolomics
		1.2 Liquid Chromatography Mass Spectrometry
		1.3 LC-MS Metabolomics Data Analysis and Interpretation
		1.4 Machine Learning Applied to Untargeted Metabolomics
		1.5 Deep Learning Applied to Untargeted Metabolomics
	2 Overview of DL Methods
	3 DL Methods for Pre- and Postprocessing Metabolomics Data
		3.1 Peakonly: A DL Model for Detecting and Integrating Peaks
		3.2 NeatMS: A DL-Based Post Processing Tool to Remove Low Quality Peaks
		3.3 NormAE: The Normalized Autoencoder for Removing Batch Effects
	4 DL Methods for Metabolite Annotation
		4.1 GNN-RT: Improving Metabolite Annotation by Predicting RT Using Graph Neural Networks
		4.2 DeepMASS: Structural Similarity Scoring of Unknown Metabolites Using Deep Neural Networks
		4.3 MS2DeepScore: A Siamese Neural Network for Predicting the Structural Similarity of MS/MS Fragmentation Spectra
		4.4 CANOPUS: A Computational Tool for Systematic Compound Class Annotation
		4.5 DarkChem: A Variational Autoencoder for Creating a Massive in Silico Library
	5 Conclusions
	References
The Search for Cancer Drivers
	1 Oncogenes, Tumor Suppressors and the Hallmarks of Cancer
	2 The Investigation of Driver Mutations
		2.1 Consensus Approaches for Driver Discovery
	3 Exploiting Pathway and Gene Set Information to Power the Discovery of Driver Mutations
	4 Identification of Driver Copy Number Alterations
	5 Identification of Driver Gene Fusions
	6 Driver Identification Through Regulatory Network Analysis
	7 Multi-omics Approaches for the Identification of Cancer Drivers
	8 Identification of Non-coding Drivers
	9 Patient-Specific Drivers and Precision Medicine
	10 Databases of Cancer Drivers
	11 Conclusions
	References
Inferring a Gene Regulatory Network from Gene Expression Data. An Overview of Best Methods and a Reverse Engineering Approach
	1 Introduction
	2 Background
		2.1 Gene Regulatory Network
		2.2 Inferring Gene Regulatory Networks
	3 Methodologies
		3.1 Information Theory Methods
		3.2 Boolean Networks
		3.3 Differential Equations
		3.4 Machine Learning
		3.5 Reverse Engineering
	4 Validation
	5 Results
	6 Conclusions
	References
Efficient Random Strategies for Taming Complex Socio-economic Systems
	1 Introduction
	2 The Peter Principle
	3 Improving Democracy by Lot
	4 Random Strategies in Financial Markets
		4.1 The ``micro'' Perspective
		4.2 The ``macro'' Perspective
		4.3 ``Micro'' Within ``macro'' Perspective
		4.4 Conclusive Remarks and Policy Suggestions
	5 Talent, Luck and Success
		5.1 The TvL Model
		5.2 The Origin of Heavy Tailed Distributions in the TvL Model
		5.3 Success in Scientific Research and Funding Policies
	6 Conclusions
	References
Critical Density for Network Reconstruction
	1 Introduction
	2 General Setting
		2.1 The Reconstruction Method
		2.2 Transition from Reconstructability to Unreconstructability
	3 Specific Cases
		3.1 Homogeneous Networks
		3.2 Core-Periphery Structure
		3.3 Arbitrary Strength Distribution
	4 The Role of Induced Self-loops
	5 Reconstructed Networks from Bankscope Data
		5.1 Dataset
		5.2 Network Reconstruction
	6 Conclusions
	References
Motif Finding Algorithms: A Performance Comparison
	1 Introduction
	2 Motif Finding Problem
		2.1 Preliminary Definitions
		2.2 Measures of Statistical Significance
		2.3 Approaches
	3 Motif Finding Tools
		3.1 FANMOD
		3.2 Kavosh
		3.3 mFinder
		3.4 MAVisto
		3.5 gtrieScanner
		3.6 NetMODE
		3.7 QuateXelero
		3.8 FaSE
		3.9 Acc-Motif
		3.10 NeMo
		3.11 PGD: Parallel Graphlet Decomposition
	4 Experimental Analysis
		4.1 Dataset Description
		4.2 Results
	5 Perspectives and Future Work
	References
Author Index