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, 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 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 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 - 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.

Lecture slides;

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: 1h. – 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.