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Bioinformatics

01OVFOV, 01OVFOQ, 01OVFPE

A.A. 2021/22

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

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 30 0 0 0 1
Teaching assistant
Espandi

Context
SSD CFU Activities Area context
ING-INF/05 6 D - A scelta dello studente A scelta dello studente
2021/22
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 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 Slack, will be used to get in touch students and teaching staff, and share information. LAB: - the course will include exercises and 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, R and Matlab. - lab will be provided both onsite and on online platforms provided by the Politecnico, according to the rectoral directives.
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, Aglaé Bassens. Addison-Wesley Professional; 1 edition (August 5, 2019); iii) others suggested during the course.
Modalità di esame: Test informatizzato in laboratorio; Prova scritta (in aula);
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
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. – 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.
Modalità di esame: Prova scritta a risposta aperta o chiusa tramite PC con l'utilizzo della piattaforma di ateneo Exam integrata con strumenti di proctoring (Respondus);
The exam can be taken by choosing one of the following two modalities: - written test (both theoretical and programming part); - individual or group project with an oral discussion about projects. In details, alternatively: 1) The written test will cover all the course topics (max score 30L/30L): - It consists of 2 open questions about course topics, and 2 exercises based on Python programming - Duration: 2h. - 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. 2) Individual or group Project (max score 30L/30): - the description of available projects will be provided by the teaching staff at the beginning of December - the available projects will span from analytical, design and implementation/optimization goals depending on the specific project. According to the specific complexity of the project, limitations on the maximum number of students in the group will be imposed by the teaching staff. - there will be no midterm deadlines for the project completion - projects can be developed without time constraints - the projects will be discussed in an oral presentation; the proper date for the presentation will be decided in agreement with the student Preparation for the project presentation and discussion: - the oral presentation will be about the project development, contest, issues, and results (slides). Questions will concern the project development, the results and theoretical related topics covered during the course. A demo will be also required. - A user manual about technical issues, as well as developed code and results, will be provided to the teaching staff no later than three days before the project presentation In case of online exam, the oral presentation will take place through the course Virtual classroom of other platforms such as Zoom or Teams. The session will be recorded to keep track of the exam.
Exam: Computer-based written test with open-ended questions or multiple-choice questions using the Exam platform and proctoring tools (Respondus);
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. – 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.
Modalità di esame: Prova scritta (in aula); Prova scritta a risposta aperta o chiusa tramite PC con l'utilizzo della piattaforma di ateneo Exam integrata con strumenti di proctoring (Respondus);
The exam can be taken by choosing one of the following two modalities: - written test (both theoretical and programming part); - individual or group project with an oral discussion about projects. In details, alternatively: 1) The written test will cover all the course topics (max score 30L/30L): - It consists of 2 open questions about course topics, and 2 exercises based on Python programming - Duration: 2h. - 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. 2) Individual or group Project (max score 30L/30): - the description of available projects will be provided by the teaching staff at the beginning of December - the available projects will span from analytical, design and implementation/optimization goals depending on the specific project. According to the specific complexity of the project, limitations on the maximum number of students in the group will be imposed by the teaching staff. - there will be no midterm deadlines for the project completion - projects can be developed without time constraints - the projects will be discussed in an oral presentation; the proper date for the presentation will be decided in agreement with the student Preparation for the project presentation and discussion: - the oral presentation will be about the project development, contest, issues, and results (slides). Questions will concern the project development, the results and theoretical related topics covered during the course. A demo will be also required. - A user manual about technical issues, as well as developed code and results, will be provided to the teaching staff no later than three days before the project presentation In case of online exam, the oral presentation will take place through the course Virtual classroom of other platforms such as Zoom or Teams. The session will be recorded to keep track of the exam.
Exam: Written test; Computer-based written test with open-ended questions or multiple-choice questions using the Exam platform and proctoring tools (Respondus);
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. – 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.
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