Computational Health Laboratory
Code 755AA
Credits 6
Learning outcomes
The purpose of this laboratory course is to introduce the computer science students to the applicative domain of computational health. Industrial scale applications will be handled with the tools acquired by the students in a 5-year course. Pharmaceutical, food and biotech industries are increasingly becoming computationally driven and skills to address these challenges are missing. The lab will teach the students a language to interact with medical doctors or scientists in the reference industries -- a prerequisite for practitioners and scientists in the field. The lab will teach with hands-on experience emerging technologies that are essential in this applicative domain and will help students navigate the plethora of available methods and technologies to select the most suitable for each problem.
Besides the technological aspects of the lab, we will also make clear connections with social and ethical aspects of computational health and how students with these skills can have an impact in the world.
Knowledge:
The course will quickly present the working language to address biological and medical concepts that one needs to understand for working into a biomedical, pharmaceutical or food computational context. This course will introduce some emerging technologies to cope with big data: natural language processing for text-mining of scientific literature, data integration from heterogeneous sources, biomarker identification, pathway analysis and eventually modeling and simulation for in silico experiments. The knowledge will be delivered through practical examples and projects to be developed during the lab. Artificial intelligence, programming, data bases, statistics and computational mathematics will be revisited through the practical solution of biomedical problems.
Syllabus:
- Computational biology, bioinformatics, medical informatics, computational health.
- Public domain knowledge: publicly available resources, text-mining, DB mining
- Data integration: *-omics levels, structured and unstructured public and proprietary data, constraints, quality check.
- Biomarker identification: stratification of patients, diagnostic tools, prognostic tools.
- Functional analysis: pathway and network biology, complexity reduction, module identification.
- Dynamic modeling: modeling technologies, simulation algorithms, hybrid strategies.
- Each item above will be introduced through real industrial case studies.
Besides the technological aspects of the lab, we will also make clear connections with social and ethical aspects of computational health and how students with these skills can have an impact in the world.
Knowledge:
The course will quickly present the working language to address biological and medical concepts that one needs to understand for working into a biomedical, pharmaceutical or food computational context. This course will introduce some emerging technologies to cope with big data: natural language processing for text-mining of scientific literature, data integration from heterogeneous sources, biomarker identification, pathway analysis and eventually modeling and simulation for in silico experiments. The knowledge will be delivered through practical examples and projects to be developed during the lab. Artificial intelligence, programming, data bases, statistics and computational mathematics will be revisited through the practical solution of biomedical problems.
Syllabus:
- Computational biology, bioinformatics, medical informatics, computational health.
- Public domain knowledge: publicly available resources, text-mining, DB mining
- Data integration: *-omics levels, structured and unstructured public and proprietary data, constraints, quality check.
- Biomarker identification: stratification of patients, diagnostic tools, prognostic tools.
- Functional analysis: pathway and network biology, complexity reduction, module identification.
- Dynamic modeling: modeling technologies, simulation algorithms, hybrid strategies.
- Each item above will be introduced through real industrial case studies.