Caricamento in corso...
Bioinformatics
01OVFYG, 01OVFXW, 04OVFYH
A.A. 2026/27
Course Language
Inglese
Degree programme(s)
Master of science-level of the Bologna process in Ingegneria Informatica (Computer Engineering) - Torino
Master of science-level of the Bologna process in Ingegneria Elettronica (Electronic Engineering) - Torino
Master of science-level of the Bologna process in Ingegneria Matematica - Torino
Course structure
| Teaching | Hours |
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Lecturers
| Teacher | Status | SSD | h.Les | h.Ex | h.Lab | h.Tut | Years teaching |
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Co-lectures
Context
| SSD | CFU | Activities | Area context | ING-INF/05 | 6 | D - A scelta dello studente | A scelta dello studente |
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2024/25
Insegnamento a scelta per la Laurea Magistrale in Ing. Elettronica, Ing. Informatica, Ing. delle Telecomunicazioni, Nanotecnologie per l’ICT, II semestre del II anno. Nel corso verranno studiate le soluzioni HW/SW per l’analisi di dati genetici forniti dalle biotecnologie di ultima generazione (es. biosensori, nanotecnologie, etc.), verranno descritte tali tecnologie e approfondite le problematiche computazionali/algoritmiche per lo sviluppo di tool-flow per analisi complesse. Verranno anche studiate soluzioni per la computazione e lo storage distribuito dei dati genetici
Hardware/Software solutions will be studied for the analysis of genetic data provided by the latest generation biotechnologies (e.g. DNA/RNA next generation sequencers, nanotechnology, etc..). During the course, it will be described the state of the art of such technologies, and it will be deeply studied computational and algorithmic issues for the development of tool-flows for complex genetic analyses (such as genetic mutations and aberrations). It will be explained Machine Learning and deep learning techniques (e.g. CNNs, Bayesian CNNs, Graph Neural Networks and Convolutional GNNs, RNNs, LSTM) and their application to biological and medical problems. Moreover, it will be presented also techniques for genetic data distributed computing. In particular, it will be introduced the Clustering programming. During the course, the basic concepts of the molecular biology will be introduced, and the programming language Python will be presented (even if every programming language will be allowed in the final project development). The purpose of the course is therefore to provide training in order to make students experts of the biomolecular/genetic issues, technologies and processing techniques the most advanced in the field of biotechnology and genetic analysis.
Lo studente deve acquisire conoscenze sulle biotecnologie di ultima generazione, capacità di ideare ed applicare soluzioni algoritmiche e computazionali efficienti a problemi biologici, conoscenza di tecniche di ottimizzazione SW su griglia/CLOUD.
The student should acquire: i) the knowledge of the latest generation biotechnologies for genetic and molecular screening, ii) the knowledge of some the most up-to-date genetic issues in the personalised medicine approach, and of the main SW solutions for complex bioinformatics analyses, iii) the knowledge of computer science techniques such as machine learning, deep learning, text mining, mathematical optimization, iv) the application of AI, statistical and computational approach to genetic and medical analysis, and the ability to design and implement reliable algorithmic solutions for biological problems, v) the experience on SW optimization techniques on cluster infrastructures.
Programmazione in linguaggi ad alto livello (es. C, C++ o Java), linguaggi di scripting.
High level language computer programming (eg C, C + + or Java), and optionally scripting languages.
- Introduzione alla Bioinformatica: Concetti di Biologia Molecolare, Requisiti algoritmici, computazionali e tecnologici, Problemi di rilevanza nel campo della ricerca, dell’industria e delle aziende
- DNA e RNA-Sequencing: Descrizione delle tecnologie di sequencing, Problematiche algoritmiche e computazionali, Principali tool utilizzati per il sequenziamento e l’analisi dei dati, Problematiche relative allo sviluppo software per analisi avanzate
- Infrastrutture HW/SW parallele e distribuite per la Bioinformatica: Soluzioni HPC, soluzioni GRID, soluzioni CLUOD, Gestione dello storage, Caso di studio: RNA-Sequencing su CLOUD
- Tecniche bioinformatiche per l’analisi dell’espressione genica: Descrizione delle principali tecnologie (es. microarray, real-time PCR, RNA-Seq, etc.) , Metodologie SW per l’analisi di espressione e descrizione dei principali tool
- Tecniche bioinformatiche per lo studio e la predizione dei processi regolativi: Tecniche SW per la predizione degli RNA target dei miRNA, Derivazione delle reti regolative.
- Introduction to the Bioinformatics: Concepts of Molecular Biology, Computational, technological and efficacy requirements of the algorithms, Relevant problems in research, industry and businesses
- DNA-, microRNA- and RNA-sequencing: Description of sequencing technologies, algorithmic and computational issues, main tools used for sequencing and data analysis, issues related to software development for advanced analyses.
- Bioinformatics techniques for the study and the prediction of regulatory processes: SW techniques for the prediction of molecular interactions, data integration and correlation, derivation of regulatory networks in Complex Systems.
- Machine Learning and Deep Learning techniques: introduction to some well known and used methodologies (e.g. Neural Networks, Random Forest, Convolution Neural Network, Graph Neural Networks and Convolutional CNNs, Recurrent Neural Networks, LSTM, etc.), application of such techniques to genetic and biological studies.
- Cluster programming: implementation of analysis pipelines on computer clusters. Job scheduling, decomposition and parallelization, optimization of computational resources.
Per questo insegnamento sono previste esercitazioni e laboratori su alcuni dei tools studiati e calcolo di prestazioni sulle loro varianti parallelizzate
The course will consist of half of the theoretical lectures and half of the laboratory sessions.
LECTURES:
- the lessons will be always video recorded
- they will be provided for both off-line and on-line study, according to the directives of the rector
- Virtual Classroom on the Polito portal or other platforms, such as Zoom of Teams, will be used for virtual learning
- social networks, such as Telegram, will be used to get in touch students and teaching staff, and share information.
LAB:
- the course will include exercises and computer lab sessions on some of the studied algorithms and on the development of new SW solutions and architectures
- the most used language will be Python, but also, optionally, Matlab, C, C++.
- lab will be provided both onsite and on online platforms provided by the Politecnico, according to the rectoral
Slide del corso e manuale da stabilire.
- Course slides,
- scientific research papers,
- web documents
- short educational movies.
- optionally, ML and deep learning books, such as i) "Deep Learning" by Ian Goodfellow, Yoshua Bengio and Aaron Courvill, MIT Press; ii) "Deep Learning Illustrated: A Visual, Interactive Guide to Artificial Intelligence" (Addison-Wesley Data & Analytics Series) by Jon Krohn, Grant Beyleveld, Aglaé Bassens. Addison-Wesley Professional; 1 edition (August 5, 2019); iii) others suggested during the course.
Modalità di esame:
Exam:
...
Esame finale scritto/orale.
Gli studenti e le studentesse con disabilità o con Disturbi Specifici di Apprendimento (DSA), oltre alla segnalazione tramite procedura informatizzata, sono invitati a comunicare anche direttamente al/la docente titolare dell'insegnamento, con un preavviso non inferiore ad una settimana dall'avvio della sessione d'esame, gli strumenti compensativi concordati con l'Unità Special Needs, al fine di permettere al/la docente la declinazione più idonea in riferimento alla specifica tipologia di esame.
Exam:
Esame finale scritto/orale.
In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.