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Bioinformatics

01OVFOV, 01OVFOQ, 01OVFPE, 04OVFNG

A.A. 2022/23

Course Language

Inglese

Course degree

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 Nanotechnologies For Icts (Nanotecnologie Per Le Ict) - Torino/Grenoble/Losanna
Master of science-level of the Bologna process in Ingegneria Matematica - Torino

Course structure
Teaching Hours
Lezioni 60
Teachers
Teacher Status SSD h.Les h.Ex h.Lab h.Tut Years teaching
Politano Gianfranco Michele Maria   Ricercatore a tempo det. L.240/10 art.24-B ING-INF/05 60 0 0 0 2
Teaching assistant
Espandi

Context
SSD CFU Activities Area context
ING-INF/05 6 D - A scelta dello studente A scelta dello studente
Valutazione CPD 2021/22
2022/23
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. Random Forest, Neural Networks, CNNs, 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.
Hardware/Software solutions will be studied for systems biology and bioinformatics analysis. The course will discuss the state of the art of such technologies, and it will analyze computational and algorithmic issues for the development of tool-flows for complex genetic analyses. It will introduce Machine Learning and deep learning techniques (e.g. CNNs, Bayesian CNNs, Deep Neural Networks, RNNs, LSTM) and their application to biological and medical problems. During the course, the basic concepts of the molecular biology will be introduced, and the programming language Python will be presented. 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 systems biology, bioinformatics and medicine.
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, iii) the knowledge of the main SW solutions for complex bioinformatics analyses, and of computer science techniques such as machine learning, deep learning, text mining, graph optimization, iv) the ability to design and implement effective and computationally efficient algorithmic solutions for biological problems, vi) the experience on SW optimization techniques on cluster infrastructures.
The student should acquire: i) the knowledge of the latest generation biotechnologies, ii) the knowledge of some the most up-to-date 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, signal processing, iv) the application of AI, statistical and computational approaches to genetic and medical analysis, and the ability to design and understand reliable algorithmic solutions for biological problems.
High level language computer programming (eg C, C + + or Java), and optionally scripting languages.
High level language computer programming (eg., C, Python), and optionally scripting languages.
- 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, SW optimization on parallel and distributed infrastructures. - 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, 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.
- 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 differences: Description of systems biology technologies, algorithmic and computational issues, main tools used for 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 life science studies. Practical examples of medical issues which benefit of heterogeneous data analysis and related pipeline: detailed formalization of the question to be answered (e.g., neurodegenerative disease staging, pathology subtype classification), data collection protocol (e.g., from inertial sensors, ECG/EEG/EMG electrodes), merging of measured data with other sources of information (e.g., genetic, anamnestic, risk factors), typical processing methods, critical interpretation of the collected evidence.
The course will include exercises and computer lab sessions on some of studied tools and on the development of new SW solutions.
The course will consist of theoretical lectures and laboratory sessions. LECTURES: - the lessons will be only provided in presence - the lessons will be eventually recorded in case of new covid restrictions. In such scenario they will be provided for both off-line and on-line study, according to the directives of the rector and Virtual Classroom on the Polito portal or other platforms, such as Zoom of Teams, will be used for virtual learning - social networks, such as Slack, will be eventually used to get in touch students and teaching staff, and share information.
Course slides, scientific research papers, web documents and short educational movies.
- 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, AglaÚ Bassens. Addison-Wesley Professional; 1 edition (August 5, 2019); iii) others suggested during the course.
ModalitÓ di esame: Test informatizzato in laboratorio; Prova scritta (in aula);
Exam: Computer lab-based test; Written test;
Alternatively, final written test or research project. In both the cases, previously detailed learning outcomes should be evaluated (see in particular points i), ii), iii) and iv)) In details: Written test on all the course topics (max score 30L/30): - It consists of 2 questions about course topics, and 2 exercises on Python programming - Duration: 2h. - Use of notes, course slides, handbooks, Python examples or exercises is forbidden Project (max score 30L/30): - Project groups should be 3-4 members. - There are no midterm deadlines for the project completion - Oral presentation is about the project development, contest, issues, and results (slides). Questions concern the project and related topics covered in the course. A demo is also required. - User manual should be provided to the professor, as well as developed code, no later than two days before the project presentation
Gli studenti e le studentesse con disabilitÓ 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'UnitÓ Special Needs, al fine di permettere al/la docente la declinazione pi¨ idonea in riferimento alla specifica tipologia di esame.
Exam: Computer lab-based test; Written test;
Exam: Quiz-based written test with video surveillance of the teaching staff; 1) The written test will cover all the course topics (max score 30L/30L): - It consists of multiple open/closed choice questions about course topics, Duration: 2h. ľ Use of notes, course slides, handbooks, Python examples or exercises will be forbidden In the case of an online exam, the written test will be performed through video surveillance on the course Virtual Classroom platform. The session will be recorded to keep track of the exam.
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.
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