FRANCESCO BARDOZZO | ALGORITHMS FOR BIOINFORMATICS

cod. 0522100039

ALGORITHMS FOR BIOINFORMATICS

	0522100039
	DEPARTMENT OF CHEMISTRY AND BIOLOGY "ADOLFO ZAMBELLI"
	EQF7
	BIOLOGY
	2024/2025

CURRICULUM BIOINFORMATICS

	OBBLIGATORIO
	YEAR OF COURSE 1
	YEAR OF DIDACTIC SYSTEM 2022
	SPRING SEMESTER

TEACHING UNITS

SSD	CFU	HOURS	ACTIVITY	TYPE OF ACTIVITY
INF/01	4	32	LESSONS	SUPPLEMENTARY COMPULSORY SUBJECTS
INF/01	2	24	LAB	SUPPLEMENTARY COMPULSORY SUBJECTS

PROFESSORS

	ROBERTO TAGLIAFERRI T Curriculum
	FRANCESCO BARDOZZO Curriculum

EXAMS

Exam	Date	Session
ALGORITMI PER LA BIOINFORMATICA	30/01/2025 - 10:00	SESSIONE ORDINARIA
ALGORITMI PER LA BIOINFORMATICA	30/01/2025 - 10:00	SESSIONE DI RECUPERO
ALGORITMI PER LA BIOINFORMATICA	20/02/2025 - 10:00	SESSIONE ORDINARIA
ALGORITMI PER LA BIOINFORMATICA	20/02/2025 - 10:00	SESSIONE DI RECUPERO

	Objectives
	THE COURSE AIMS TO PROVIDE THE METHODOLOGICAL AND TECHNOLOGICAL TOOLS FOR THE ANALYSIS OF BIOINFORMATICS DATA. IN PARTICULAR, STUDENTS WILL HAVE TO MASTER THE METHODOLOGIES AND ALGORITHMS FOR THE ALIGNMENT OF NUCLEOTIDE AND PROTEIN SEQUENCES, IN PAIRS OR MULTIPLE, LOCAL OR GLOBAL, FOR THE RECONSTRUCTION OF PHYLOGENETIC TREES, FOR THE CLUSTERING AND CLASSIFICATION OF BIOINFORMATICS DATA. (E.G., EXPRESSION DATA, FUNCTIONAL DATA FROM GENOMICS AND PROTEOMICS, FOR MEDICAL DIAGNOSTICS, ETC.). THROUGH THE THEORETICAL LESSONS AND LABORATORY ACTIVITIES, THE STUDENTS WILL ACQUIRE THE ADEQUATE SKILLS TO USE THE LINUX COMMAND LINE OPERATING SYSTEM, TO MAKE CODING, TO USE AND MODIFY MACHINE AND DEEP LEARNING PROGRAMS, AND TO SOLVE PROBLEMS OF BIOLOGICAL INTEREST IN STATISTICAL PATTERN RECOGNITION.

THE COURSE AIMS TO PROVIDE THE METHODOLOGICAL AND TECHNOLOGICAL TOOLS FOR THE ANALYSIS OF BIOINFORMATICS DATA. IN PARTICULAR, STUDENTS WILL HAVE TO MASTER THE METHODOLOGIES AND ALGORITHMS FOR THE ALIGNMENT OF NUCLEOTIDE AND PROTEIN SEQUENCES, IN PAIRS OR MULTIPLE, LOCAL OR GLOBAL, FOR THE RECONSTRUCTION OF PHYLOGENETIC TREES, FOR THE CLUSTERING AND CLASSIFICATION OF BIOINFORMATICS DATA. (E.G., EXPRESSION DATA, FUNCTIONAL DATA FROM GENOMICS AND PROTEOMICS, FOR MEDICAL DIAGNOSTICS, ETC.).
THROUGH THE THEORETICAL LESSONS AND LABORATORY ACTIVITIES, THE STUDENTS WILL ACQUIRE THE ADEQUATE SKILLS TO USE THE LINUX COMMAND LINE OPERATING SYSTEM, TO MAKE CODING, TO USE AND MODIFY MACHINE AND DEEP LEARNING PROGRAMS, AND TO SOLVE PROBLEMS OF BIOLOGICAL INTEREST IN STATISTICAL PATTERN RECOGNITION.

	Prerequisites
	BASICS OF MATHEMATICS, STATISTICS, AND COMPUTER SCIENCE.

	Contents
	1. ANALYSIS AND VISUALIZATION OF OF BIOLOGICAL DATA. DATABASES OF BIOLOGICAL INTEREST. 4H 2. ALIGNMENT OF SEQUENCES; SCORE MATRICES AND DOT-PLOT; MULTIPLE ALIGNMENTS. 4H 4. THE EVOLUTION OF PROTEINS; DATABASE SEARCH FOR SIMILARITY; SIGNIFICANCE OF ALIGNMENT; ASSESSMENT. 2H 5. ALGORITHMS OF ALIGNMENT: DYNAMIC PROGRAMMING, FASTA, BLAST AND HMM. 6H 6. THE STRUCTURE AND INTERPRETATION OF PHILOGENETIC TREES; MOLECULAR EVOLUTION AND ITS CONSEQUENCES; CONSTRUCTION OF PHILOGENETIC TREES. 4H 7. GENE EXPRESSION ANALYSIS: GENE EXPRESSION DATA AND SOME ANALYSIS TECHNIQUES 4H 8. CLUSTERING ALGORITHMS AND STATISTICAL METHODS: PREPARATION OF GENE EXPRESSION DATA; SOME TECHNIQUES OF ANALYSIS BASED ON CLUSTERING; CLASSIFICATION OF SAMPLES WITH EXPRESSION DATA. 6H 9. MACHINE LEARNING & AI ALGORITHMS. 2H APPLICATION PART: 1. WRITING COMMAND LINE PROGRAMS IN LINUX: 4 H 2. THE PYTHON LANGUAGE, BASIC ELEMENTS 4H 3. CLASSIFICATION AND CLUSTERING ALGORITHMS IN PYTHON 4H 3. PHYLOGENETIC TREE CONSTRUCTION 4H 4. A CASE STUDY: SINGLE CELL DATA ANALYSIS 8H

Contents

1. ANALYSIS AND VISUALIZATION OF OF BIOLOGICAL DATA. DATABASES OF BIOLOGICAL INTEREST. 4H
2. ALIGNMENT OF SEQUENCES; SCORE MATRICES AND DOT-PLOT; MULTIPLE ALIGNMENTS. 4H
4. THE EVOLUTION OF PROTEINS; DATABASE SEARCH FOR SIMILARITY; SIGNIFICANCE OF ALIGNMENT; ASSESSMENT. 2H
5. ALGORITHMS OF ALIGNMENT: DYNAMIC PROGRAMMING, FASTA, BLAST AND HMM. 6H
6. THE STRUCTURE AND INTERPRETATION OF PHILOGENETIC TREES; MOLECULAR EVOLUTION AND ITS CONSEQUENCES; CONSTRUCTION OF PHILOGENETIC TREES. 4H
7. GENE EXPRESSION ANALYSIS: GENE EXPRESSION DATA AND SOME ANALYSIS TECHNIQUES 4H
8. CLUSTERING ALGORITHMS AND STATISTICAL METHODS: PREPARATION OF GENE EXPRESSION DATA; SOME TECHNIQUES OF ANALYSIS BASED ON CLUSTERING; CLASSIFICATION OF SAMPLES WITH EXPRESSION DATA. 6H
9. MACHINE LEARNING & AI ALGORITHMS. 2H
APPLICATION PART:
1. WRITING COMMAND LINE PROGRAMS IN LINUX: 4 H
2. THE PYTHON LANGUAGE, BASIC ELEMENTS 4H
3. CLASSIFICATION AND CLUSTERING ALGORITHMS IN PYTHON 4H
3. PHYLOGENETIC TREE CONSTRUCTION 4H
4. A CASE STUDY: SINGLE CELL DATA ANALYSIS 8H

	Teaching Methods
	LESSONS, EXERCITATIONS AND LAB ACTIVITY

	Verification of learning
	ORAL EXAM ON ALL THE CONTENTS OF THE COURSE. A PROJECT TO VERIFY THE CAPACITY OF APPLYING MODELS TO BIOINFORMATICS DATA. ORAL SESSION AFTER THE END OF THE COURSE. PROJECT PRESENTATION OF ON A TOPIC OF THE METHODS APPLIED TO BIOINFORMATICS DATA ANALYSIS PREVIOUSLY AGREED BETWEEN THE STUDENT AND THE LECTURERS DURING THE COURSE. THE ORAL EXAMINATION WITH THE DISCUSSION OF THE PROJECT WORK ARE AIMED AT ASSESSING THE LEVEL OF MASTERY OF THE DISCIPLINE. THIS LEVEL IS EXPRESSED ON THE BASIS OF THE SCALE OF MARKS FROM 18/30 (LIMITED KNOWLEDGE OF THE SUBJECT) TO 30/30 LODE (THE CANDIDATE DEMONSTRATES SIGNIFICANT MASTERY OF THE CONTENTS). BOTH EXAM TESTS CONTRIBUTE EQUALLY TO THE FINAL ASSESSMENT. THE DURATION OF THE ORAL EXAM IS ABOUT 20 MINUTES, AS IS THE DURATION OF THE PRESENTATION OF THE PROJECT.

Verification of learning

ORAL EXAM ON ALL THE CONTENTS OF THE COURSE. A PROJECT TO VERIFY THE CAPACITY OF APPLYING MODELS TO BIOINFORMATICS DATA.
ORAL SESSION AFTER THE END OF THE COURSE. PROJECT PRESENTATION OF ON A TOPIC OF THE METHODS APPLIED TO BIOINFORMATICS DATA ANALYSIS PREVIOUSLY AGREED BETWEEN THE STUDENT AND THE LECTURERS DURING THE COURSE.
THE ORAL EXAMINATION WITH THE DISCUSSION OF THE PROJECT WORK ARE AIMED AT ASSESSING THE LEVEL OF MASTERY OF THE DISCIPLINE. THIS LEVEL IS EXPRESSED ON THE BASIS OF THE SCALE OF MARKS FROM 18/30 (LIMITED KNOWLEDGE OF THE SUBJECT) TO 30/30 LODE (THE CANDIDATE DEMONSTRATES SIGNIFICANT MASTERY OF THE CONTENTS). BOTH EXAM TESTS CONTRIBUTE EQUALLY TO THE FINAL ASSESSMENT. THE DURATION OF THE ORAL EXAM IS ABOUT 20 MINUTES, AS IS THE DURATION OF THE PRESENTATION OF THE PROJECT.

	Texts
	MARKETA ZVELEBIL, JEREMY O. BAUM: UNDERSTANDING BIOINFORMATICS, GARLAND SCIENCE 2008 SLIDES AND OTHER CONTENTS AVAILABLE IN TEAMS, UPLOADED BY THE LECTURERS

	More Information
	A REGULAR COURSE FREQUENCY IS REQUIRED FOLLOWING THE INDICATIONS OF THE TEACHING AREA BOARD.

BETA VERSION Data source ESSE3 [Ultima Sincronizzazione: 2024-12-13]

FRANCESCO BARDOZZO | ALGORITHMS FOR BIOINFORMATICS