First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
DATA SCIENCE
Course unit
BIOLOGICAL DATA
SCP7079337, A.A. 2018/19

Information concerning the students who enrolled in A.Y. 2017/18

Information on the course unit
Degree course Second cycle degree in
DATA SCIENCE
SC2377, Degree course structure A.Y. 2017/18, A.Y. 2018/19
N0
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Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination BIOLOGICAL DATA
Website of the academic structure http://datascience.scienze.unipd.it/2018/laurea_magistrale
Department of reference Department of Mathematics
Mandatory attendance No
Language of instruction English
Branch PADOVA
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Lecturers
Teacher in charge SILVIO TOSATTO BIO/10

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Educational activities in elective or integrative disciplines BIO/09 Physiology 1.0
Educational activities in elective or integrative disciplines BIO/10 Biochemistry 2.0
Educational activities in elective or integrative disciplines BIO/11 Molecular Biology 1.0
Educational activities in elective or integrative disciplines MED/04 General Pathology 2.0

Course unit organization
Period First semester
Year 2nd Year
Teaching method frontal

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Lecture 6.0 48 102.0 No turn

Calendar
Start of activities 01/10/2018
End of activities 18/01/2019

Examination board
Examination board not defined

Syllabus
Prerequisites: Basic knowledge of computer science, optimization methods and machine learning. Python programming language.
Target skills and knowledge: The course intends to communicate basic knowledge on different types of biological data, such as sequence, structure, network and literature. Moreover, it intends to enable the student to autonomously develop a research project in structural bioinformatics, defining the state of the art for an open problem and providing an attempt to solve it through the extension of existing software libraries and the critical evaluation of obtained results.
Examination methods: The exam covers three separate parts, which have to be all passed: (relative weights in parenthesis)
1) Test for the practicals (ca. 20%)
2) Project (ca. 50%)
3) Project presentation and critical evaluation (ca. 30%)
Assessment criteria: 1) understanding of concepts and algorithms presented in class
2) the ability to apply the described concepts on real problems
3) the critical capacity of being able to use the methods in the most appropriate ways, choosing between the alternatives
4) the ability to develop reusable software by extending existing libraries
5) the ability for critical presentation and discussion
Course unit contents: The course consists of four parts, corresponding to different types of biological data:

1) Sequences
1.1) DNA and proteins
1.2) Databases
1.3) Alignments

2) Structures
2.1) Protein folding
2.2) Databases
2.3) Structure prediction

3) Interaction networks
3.1) Biological interactions
3.2) Databases
3.3) Emergent properties

4) Literature
4.1) Scientific papers
4.2) Databases
4.3) Text mining
Planned learning activities and teaching methods: The course consists of lectures, practical computer exercises, lecture note contribution and the development of a project and presentation of the same with critical discussion. The exercises are intended to familiarize the student with software libraries to use for a bioinformatics project on a current problem differentiated for each group. The project presentation will require a discussion in which to bring out the strengths and weakness of the chosen solution.
Additional notes about suggested reading: Many materials for the course are made available on the E-learning site. These include the transparencies of the course (as soon as available) and audio recordings (podcasts), lecture notes and literature used for the projects.
Textbooks (and optional supplementary readings)

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Laboratory
  • Case study
  • Working in group
  • Problem solving
  • Loading of files and pages (web pages, Moodle, ...)

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)