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.

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.

High level language computer programming (eg C, C + + or Java), 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.

The course will include exercises and computer lab sessions on some of studied tools and on the development of new SW solutions.

Course slides, scientific research papers, web documents and short educational movies.

Modalità 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 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.

Modalità di esame: Prova scritta tramite PC con l'utilizzo della piattaforma di ateneo;

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.

Modalità di esame: Prova scritta (in aula); Prova scritta tramite PC con l'utilizzo della piattaforma di ateneo;

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.