Overview Bioinformatics develops algorithms and programs to simulate biochemical processes and analyze molecular data. It combines knowledge of biochemical and molecular biological processes in organisms in applied computer science and data management as well as analysis. As part of the system biology, bioinformatics supports both research as well as industrial development and production. Your education will provide you with excellent prospects at the interface between basic research and development.Contact usContact us!Elisabeth BeckElisabeth Holzmann, Bakk.techn.Johanna BauerBarbara PhilippMuthgasse 621190 ViennaT:+43 1 606 68 77-3600F: +43 1 606 68 77-3609bioengineering@fh-campuswien.ac.atMap Muthgasse (Google Maps)Office hours during semesterMon to Thu, 4.30 p.m.-6.00 p.m. By appointmentMon to Thu, 10.00 a.m.-6.00 p.m.Fri, 10.00 a.m.-1.00 p.m.Duration of course4 SemesterOrganisational formpart-time120ECTSLanguage of instruction German22Study placesFinal degreeMaster of Science in Engineering (MSc)Application period for academic year 2021/221st January 2021 to 15th June 2021 tuition fee / semester:€ 363,361+ ÖH premium + contribution2 1 Tuition fees for students from third countries € 727,- per semester 2 for additional study expenses (currently up to € 83,- depending on degree program and year) What you can offer You have a background in natural sciences, are enthusiastic about IT and already possess basic knowledge.You see your future in combining both and using your IT skills to process and analyze the flood of data in life sciences and present it optimally and understandably.You are an analytical and process-oriented thinker. You enjoy solution-oriented work at the interface between different disciplines.You want to achieve professional success working on projects in a team and are open to management responsibilities.You can also imagine providing independent services.Average English skills are expected. Language of instruction is German What we offer you Your education and research benefit from our close partnerships with the University of Natural Resources and Life Sciences, Vienna (BOKU) and the Vienna Institute of Biotechnology (VIBT), who share the campus with us, as well as our strong network in the industry.In addition to experts from the industry, researchers from the BOKU also teach in our degree program and contribute their application-oriented know-how.Selected courses are offered in English, the technical language of life sciences.We are currently building an IT infrastructure that should be available as an internet service for public researchers as well as our students.Numerous R&D projects in the degree program offer you the opportunity to work with cutting-edge applications and to make valuable contacts for your future career.Practical relevance is also guaranteed at our Campus Lecture evenings, which are open to all and feature contributions from prominent experts. What makes this degree program special The degree program focuses on molecular biology and is tailored specifically to the needs of pharmaceutical production. Bioinformatics is already an indispensable research tool for biotechnological methods in order to cope with the flood of data in life sciences. The deciphering of the human genome in genomic research is only one possible application. The main goal is to develop new medications based on the analysis of human genes.Huge databases of active ingredients must be searched for suitable candidates.Bioinformatics methods also make it possible to optimize and individualize therapies through the comparison of entire genomes.These tasks are even many times more complex than the makeup of the DNA sequence and can only be solved with the help of bioinformatics.The need for bioinformaticians in the biotech industry is growing rapidly.With our production-oriented education, you will be highly sought after. What you will learn in the degree program As an interdisciplinary science, bioinformatics provides solutions for biological issues by applying informatics methods.The program combines applied informatics, data management and data evaluation with molecular biology, biochemistry and bioinformatics.In applied informatics you will learn about programming, operating systems, networks and database systems.In order to effectively manage and evaluate data, you will acquire knowledge in mathematics, statistics, algorithms and software development.In the field of molecular biology and biochemistry you will learn to conduct sequence comparisons and make structure and function predictions, as well as how high-throughput technologies function.Applied bioinformatics encompasses the application of bioinformatics and chemistry informatics.Management and corporate management skills will round out your education.You will apply the methods of scientific work within the framework of your master’s dissertation. Curriculum 1. Semester LectureSWSECTSSelected chapters of Mathematics VOSelected chapters of Mathematics VOLector: Univ.-Prof. Dipl.-Ing. Dr. Werner Timischl1SWS2ECTSLecture contents1 Discrete mathematics: permutations and combinations, time complexity of algorithms, recursions. 2 Matrices: First and Second Kind of Spatial Representation; calculations with vectors and matrices; systems of linear equations; eigenvalues and eigenvectors. 3 Selected methods of multivariate statistics: hierarchical classification; principal component analysis. 4 Markov-models: Markov-chains; Hidden-Markov-models.Assessment methodsWritten examinationTeaching methodsLecture combined with exercies.12Proteomics ILVProteomics ILVLector: Lukas Janker, MSc1.5SWS3ECTSLecture contentsProteomics: opportunities and aims Methods, focussing on mass spectrometry Statistical methods for quality control, motivation for and building of public repositories Databases/ontologies Bioinformatic applications making use of large data setsAssessment methodswritten examTeaching methodsLecture, working with computer1.53Statistics ILVStatistics ILVLector: Eva Valerie Lehner, BSc, Dipl.-Ing. Dr. Alexandra Posekany1.5SWS3ECTSLecture contentsintroduction to statistics and probability using R; exploratory data analysis, statistical estimation, graphics, Bayes' theorem, important distributions, hypothesis testing, ANOVA and linear regressionAssessment methodsexercises, written exam, in calls participationTeaching methodslecture, presentation, work with PC, group work, exercises1.53Databases VODatabases VOLector: DI. Dr.techn. Dominik Ertl, FH-Prof. FH-Hon.Prof. Priv.-Doz. Mag. DI. DI. Dr.techn. Karl Michael Göschka2SWS4ECTSLecture contents• Principles of architecture and database systems • Transaction concept and SQL-core • Entity Relationship (ER) Model • Relational Model • Relational database design • Database implementation with SQL-DDL • Practical design assignment in small groupsAssessment methodsContinuous assessments throughout the content presentation (grading of presentations) Written exam Distance learning: Practical assignmentTeaching methodsLecture Student presentations followed by discussions Practical assignments in small groups Open distance learningLanguageGerman24Data Mining and Visualization ILVData Mining and Visualization ILVLector: Dipl.-Ing. Barbara Lederer1SWS2ECTSLecture contentsBasic concepts of Data Mining Introduction into Visualization Pros and Cons in representationsAssessment methodsThe examination consists of attendance, active participation as well as the grading of the exercise work.Teaching methodsLecture plus exerciseLanguageGerman-English12Introduction to Programming ILVIntroduction to Programming ILVLector: DI Norbert Auer2.5SWS5ECTSLecture contentsExtending the knowledge in the programming language Python in the context of real biological problems. * Software tools: Git * Container Classes (Listen, Dictionaries) * Object-orientated programming (Classes, Inheritance, ...) * File handling * Generators * Special modules: BiopythonAssessment methodsWritten exam at the end of the course and evaluation of the exercisesTeaching methodsLectures Homework ExercisesLanguageGerman2.55Introduction to Linux and Shellscripting ILVIntroduction to Linux and Shellscripting ILVLector: Ing. DI (FH) Nadine Elpida Tatto1SWS2ECTSLecture contentsThe operating system Linux is essential in bioinformatics. Basic knowledge in the use of Linux (Ubuntu) is assumed. The lecture focuses on shell scripting and the usage of sed and awk. The named tools will be used for the processing of typical file formats in bioinformatics.Assessment methodsWritten exam, complete and punctual delivery of exercises. Both count 50/50 for the gradeTeaching methodsLecture and exercises during lecture time and at homeLanguageGerman12Basics of Algorithms VOBasics of Algorithms VOLector: Ing. Christian Binder, BSc1SWS2ECTSLecture contentsIntroduction to algorithms, graph theory, basic common and bioinformatics algorithmsAssessment methodsWritten test (multiple choice)Teaching methodsSlides and practical examplesLanguageGerman-English12Transcriptomics and Genomics ILVTranscriptomics and Genomics ILVLector: FH-Prof.in Mag.a Dr.in Alexandra Graf, Lisa Tucek, BSc.2SWS4ECTSLecture contents1) Acquire knowledge on selected bioinformatics chapters (Next Generation Sequencing, ChIP-Seq, RNA-Seq) ans 2) Application of relevant bioinformatics tools for the analysis of associated data 3) using script languages under Linux, such as Bash, awk and perl.Assessment methods40 % Workshop: Hand in a short program which solved a given task 60% Lecture: Written examTeaching methods- Introductions and explanations (lecture) - Exercises using the computerLanguageGerman24Transcriptomics and Genomics Practice UETranscriptomics and Genomics Practice UELector: FH-Prof.in Mag.a Dr.in Alexandra Graf, Lisa Tucek, BSc.1.5SWS3ECTSLecture contentsThe exercise should give students the option of working with software and programs introduced in the lecture Transcriptomics and Genomics.Assessment methodsPractical examplesTeaching methodsMoodle, Linux server, example scriptsLanguageGerman1.53 2. Semester LectureSWSECTSMachine Learning Methods ILVMachine Learning Methods ILVLector: Dipl.-Ing. Dr.techn. David Steyrl1SWS2ECTSLecture contentsUpon successful completion, students will have knowledge in: - historical outline of machine-learning development - key terms of the field (AI, ML, ...) - important concepts (bias-variance trade off, cross-validation, ...) - overview of important algorithms in the field - basic programming in python - application of ML algorithms to real-world data Every lesson of this seminar consists of two parts. The first half is spent on theory, whereas the second half is used to expand the theoretical knowledge by practical exercises in Python.Assessment methodsWritten exam (multiple choice online in moodle, 30min). No aids are allowed to be used.Teaching methodsLecture + Exercise: - Lecture: online, videos on moodle - Exercise: Jupyter notebooks, solutions, online discussions of the exercisesLanguageEnglish12Selected Chapters of Bioinformatics SESelected Chapters of Bioinformatics SELector: FH-Prof.in Mag.a Dr.in Alexandra Graf, DI (FH) Anton Grünberg1SWS2ECTSLecture contentsThe seminar is meant to give students an overview over the various fields of bioinformatics. Experts from different fields will be invited to talk about their experiences and projects.Assessment methodsWritten exam at the end of the course.Teaching methodsSeminar Talks and DiscussionsLanguageGerman-English12Database Systems ILVDatabase Systems ILVLector: DI. Dr.techn. Dominik Ertl, FH-Prof. FH-Hon.Prof. Priv.-Doz. Mag. DI. DI. Dr.techn. Karl Michael Göschka1.5SWS3ECTSLecture contents- Database query with SQL - Persistency problems, database programming, cursor concept - Elaborating the requirements and needs for computer scientists that use database systems and focuse on Bio-informatics - Programming with Perl: Perl-DBI, CGI - Practical Programming tasks in small groupsAssessment methodsContinuous assessments throughout the course (grading of presentations and participation) Distance learning: Practical assignmentTeaching methodsStudent presentations followed by discussions Practical assignments in small groupsLanguageGerman1.53Medical Analysis of Genoms VOMedical Analysis of Genoms VOLector: DI Dr. Albert Kriegner, DI(FH) Dr. Stephan Pabinger1SWS2ECTSLecture contents-introduction in medical genome analysis -quality evaluation of the data -identification of variations -annotation of variations -interpretation of data -methods for "genetic testing"Assessment methods- presence and active participation - presentations done by the studentsTeaching methods- lecture with exercise - presentations done by the students and discussionLanguageGerman12Specific Statistics Practice UESpecific Statistics Practice UELector: Eva Valerie Lehner, BSc, Dipl.-Ing. Dr. Alexandra Posekany1SWS2ECTSLecture contentsGeneral topics of statistics: statistical modeling, selection of models generalized models (logistic regression) bayesian estimate and modeling creation of interactive apllicatins with R ShinyAssessment methodscontinuous assessments throughout the course and final examinationTeaching methodsApplied practice with "R"LanguageGerman12Structure Prediction in Biopolymeres VOStructure Prediction in Biopolymeres VOLector: Dr. Sven Brüschweiler, Dr. Tanja Gesell1SWS2ECTSLecture contentsThis lecture gives an introduction to basic principles of protein and RNA structure using top-down and bottom-up approaches for structure predictions. Topics include: - Introduction of experimental structure determination methods of biopolymers - Alignment methods and programs for structure prediction methods - In silicon prediction of RNA structure using dynamic programming - Genome wides screens of RNA structure - In silicon prediction of protein secondary and tertiary structure - Visualisations of RNA and Protein structuresAssessment methodsPractical sessions and a project at the end of the course (50% each).Teaching methodstheoretical and practical exercisesLanguageGerman-English12Data Analysis Laboratory LBData Analysis Laboratory LBLector: FH-Prof.in Mag.a Dr.in Alexandra Graf, Ing. DI (FH) Dr. Harald Kühnel, MSc, Katharina Seiberl, BSc MSc2SWS4ECTSLecture contentsOxford Nanopore data will be analysed by chosen appropriate methods and establishing an analysis pipeline. In the wet lab DNA will be extracted and sequenced. The created data will be analysed with the method that was established with the literature data.Assessment methodsImmanent examination, (protocol and results of analysis)Teaching methodsPresentation, practical in the wet-lab and in-silicoLanguageGerman24Master Thesis Preparation SEMaster Thesis Preparation SELector: FH-Prof.in Mag.a Dr.in Alexandra Graf0.5SWS1ECTSLecture contentsForm and features of the written thesis. Presentation techniques.Assessment methods-Teaching methodsTalk - discussionLanguageGerman-English0.51Softwaredevelopment ILVSoftwaredevelopment ILVLector: DI Christian Heiderer3SWS6ECTSLecture contentsThis course looks at the basics of programming in C++. After repeating the basic language constructs of C we will shortly focus on working with arrays, pointers and references. Switching to C++, we will learn through hands-on programming effective application of following important fields: - Usage of the C++ Standard Library: Strings and container classes/templates - Data modelling with C++ classes - Implementation of Graphical User Interfaces (GUI) using QT Additional non-C++ related targets are: - Understanding of the Software Development Process - Efficient work in small software projects - Writing easy-to-read and easy-to-maintain codeAssessment methodsSmall self-written program at end of courseTeaching methodsPresentations and discussions, hands-on practice on the PC, discussions.LanguageGerman-English36Applied Programming Practice ILVApplied Programming Practice ILVLector: Ing. Christian Binder, BSc, Msc Josef Moser, BSc3SWS6ECTSLecture contents- Introduction to programming using C/C++ with Linux - Usage of Compilers and Debuggers - Implementation of Examples - Excursus into object-oriented Progamming and high Performance computing - From the basic idea to the implementationAssessment methodsrecurring tests, exercises, presentation, collaborationTeaching methods- lecture - presentation and collaboration - practice during the lecture - exercisesLanguageGerman36 3. Semester LectureSWSECTS Automation ILV 36 Biotechnological Seminar SE 0.51 Business Plan and Cost Accounting ILV 24 Validification of Software and Medical Devices VO 12 Innovation and Entrepreneurship ILV 12 Clinical Bioinformatics ILV 1.53 Metagenom Analysis ILV 12 Molecular Design ILV 1.53 Network and Internet Technologies ILV 12 Patenting ILV 12 Computational Systems Biology ILV 1.53 4. Semester LectureSWSECTS Master Thesis Supervision AP 01 Master Thesis MT 028 Master Thesis Seminar SE 11Semester datesSummer semester 2020: 10th February to 12th July 2020Winter semester 2020/21: 17th August 2020 to 31st January 2021 Number of teaching weeks20 per semester Times 6.00 p.m.-9.15 p.m. (ca. four times Mon to Fri), Sat ca. every two weeks (all day)Language of instruction German How you benefit As an expert in bioinformatics, you will manage and analyze data with high-throughput analysis methods and model structures and functions of biomolecules. You will find a career in the following occupational fields: Biotechnologische ForschungsunternehmenBiopharmazeutische IndustrieIndustrielle Biotechnologie Medizinische und molekularbiologische ForschungBioinformatik-DienstleistungsanbieterInnen Admission Admission requirements Bachelor degree in natural sciences-technology or similar qualification from an institute of higher education with a total of 180 ECTS creditsWith at least > 13 ECTS credits in computer science, such as Introduction to Bioinformatics, Basics databases and operating systems, programming. > 13 ECTS credits in technical subjects such as process engineering, principals of bioprocess technology, measurement and control technology. More information is available upon request. Equivalent certification from abroadRegulation for the admission of third country citizensInformation for applicants with non-Austrian (school) certificatesEquivalence is determined by international agreements, validation or in individual cases a decision by the head of the academic section. Application There are 18 places available in the master's degree program in Bioinformatics every two years. The ratio of places to applicants is currently around 1:1.5 To apply you will require the following documents:Bachelor/diploma certificate or equivalent certificate from abroadCertificates from abroad as well as a description of the courses and exemplary documents must be submitted as certified translations. Letters of recommendation from teachers from the institute abroad will help the head of the academic section to assess whether the admission requirements have been fulfilled.List of courses completed or transcript Letter of motivation CV in table form Please note:It is not possible to save incomplete online applications. You must complete your application in one session. Your application will be valid as soon as you upload all of the required documents and certificates. In the event that some documents (e.g. references) are not available at the time you apply, you may submit these later via email, mail or in person by no later than the start of the degree program. Admission procedure The admission procedure consists of a written test and an interview with the admission committee.AimThe aim is to ensure places are offered to those persons who complete the multi-level admission procedure with the best results. The tests are designed to assess the skills needed for an applicant's chosen profession.ProcedureThe written admission test assesses the applicant's knowledge of programming, molecular biology and statistics. Applicants then undergo an admission interview on the same day to provide a first impression of their personal aptitude. The qualities interviewers are looking for include professional motivation, an understanding of the profession, performance, time management. Points are assigned to each section of the test.CriteriaThe criteria for acceptance are based solely on performance. The geographical origin of the applicant has no influence on the admission decision. The admission requirements must be met in all cases. Applicants are evaluated according to the following weighting system: > Written admission test (60%) > Admission interview (40%) The study places are awarded at the latest in mid-July based on this ranking. The process as a whole and all test and assessment results from the admission procedure are documented in a transparent and verifiable manner.Written test and interview May and June 2021Planned start of the first semester (WS 2021/22) mid August Studying with disabilities If you have any questions regarding accessibility or if you have a specific need in the admission procedure due to an impairment, please contact Mag.a Ursula Weilenmann for organizational reasons as early as possible atbarrierefrei@fh-campuswien.ac.at.Since we try to take into account individual needs due to disabilities when conducting the written admission test, we ask you to indicate in your online application to Mag.a Weilenmann in which form you require support.Your contact person in the department Gender & Diversity ManagementMag.a Ursula Weilenmann, Mitarbeiterinbarrierefrei@fh-campuswien.ac.athttp://www.fh-campuswien.ac.at/barrierefrei Contact > FH-Prof. DI Dr. Michael Maurer Head of Degree Program Bioengineering, Bioinformatics, Biotechnological Quality Management, Bioprocess Engineering T: +43 1 606 68 77-3601michael.maurer@fh-campuswien.ac.at Secretary's office Elisabeth BeckElisabeth Holzmann, Bakk.techn.Johanna BauerBarbara PhilippMuthgasse 621190 ViennaT:+43 1 606 68 77-3600F: +43 1 606 68 77-3609bioengineering@fh-campuswien.ac.atMap Muthgasse (Google Maps)Office hours during semesterMon to Thu, 4.30 p.m.-6.00 p.m. By appointmentMon to Thu, 10.00 a.m.-6.00 p.m.Fri, 10.00 a.m.-1.00 p.m.New study location from winter semester 2022/23 The Department of Applied Life Sciences will move to the newly built House of Science & Engineering at Favoritenstraße 222, 1100 Vienna in the summer of 2022. Teaching staff and research staff > FH-Prof.in Mag.a Dr.in Alexandra Graf Research Staff> Ing. DI (FH) Dr. Harald Kühnel, MSc Academic Staff Cooperations and Campusnetzwerk We work closely with numerous industrial companies, universities such as the University of Natural Resources and Life Sciences, Vienna (BOKU), the Austrian Centre of Industrial Biotechnology (ACIB) and other research institutes. This guarantees you strong contacts for internships, employment or participation in research and development activities. You can find information about our cooperation activities and much more at Campusnetzwerk.It’s well worth visiting the site as it may direct you to a new job or interesting event held by our cooperation partners!Campusnetzwerk Downloads and Links Information Folder Bioinformatics Master (PDF 80 KB)Folder Applied Life Sciences (PDF 816 KB)
1. Semester LectureSWSECTSSelected chapters of Mathematics VOSelected chapters of Mathematics VOLector: Univ.-Prof. Dipl.-Ing. Dr. Werner Timischl1SWS2ECTSLecture contents1 Discrete mathematics: permutations and combinations, time complexity of algorithms, recursions. 2 Matrices: First and Second Kind of Spatial Representation; calculations with vectors and matrices; systems of linear equations; eigenvalues and eigenvectors. 3 Selected methods of multivariate statistics: hierarchical classification; principal component analysis. 4 Markov-models: Markov-chains; Hidden-Markov-models.Assessment methodsWritten examinationTeaching methodsLecture combined with exercies.12Proteomics ILVProteomics ILVLector: Lukas Janker, MSc1.5SWS3ECTSLecture contentsProteomics: opportunities and aims Methods, focussing on mass spectrometry Statistical methods for quality control, motivation for and building of public repositories Databases/ontologies Bioinformatic applications making use of large data setsAssessment methodswritten examTeaching methodsLecture, working with computer1.53Statistics ILVStatistics ILVLector: Eva Valerie Lehner, BSc, Dipl.-Ing. Dr. Alexandra Posekany1.5SWS3ECTSLecture contentsintroduction to statistics and probability using R; exploratory data analysis, statistical estimation, graphics, Bayes' theorem, important distributions, hypothesis testing, ANOVA and linear regressionAssessment methodsexercises, written exam, in calls participationTeaching methodslecture, presentation, work with PC, group work, exercises1.53Databases VODatabases VOLector: DI. Dr.techn. Dominik Ertl, FH-Prof. FH-Hon.Prof. Priv.-Doz. Mag. DI. DI. Dr.techn. Karl Michael Göschka2SWS4ECTSLecture contents• Principles of architecture and database systems • Transaction concept and SQL-core • Entity Relationship (ER) Model • Relational Model • Relational database design • Database implementation with SQL-DDL • Practical design assignment in small groupsAssessment methodsContinuous assessments throughout the content presentation (grading of presentations) Written exam Distance learning: Practical assignmentTeaching methodsLecture Student presentations followed by discussions Practical assignments in small groups Open distance learningLanguageGerman24Data Mining and Visualization ILVData Mining and Visualization ILVLector: Dipl.-Ing. Barbara Lederer1SWS2ECTSLecture contentsBasic concepts of Data Mining Introduction into Visualization Pros and Cons in representationsAssessment methodsThe examination consists of attendance, active participation as well as the grading of the exercise work.Teaching methodsLecture plus exerciseLanguageGerman-English12Introduction to Programming ILVIntroduction to Programming ILVLector: DI Norbert Auer2.5SWS5ECTSLecture contentsExtending the knowledge in the programming language Python in the context of real biological problems. * Software tools: Git * Container Classes (Listen, Dictionaries) * Object-orientated programming (Classes, Inheritance, ...) * File handling * Generators * Special modules: BiopythonAssessment methodsWritten exam at the end of the course and evaluation of the exercisesTeaching methodsLectures Homework ExercisesLanguageGerman2.55Introduction to Linux and Shellscripting ILVIntroduction to Linux and Shellscripting ILVLector: Ing. DI (FH) Nadine Elpida Tatto1SWS2ECTSLecture contentsThe operating system Linux is essential in bioinformatics. Basic knowledge in the use of Linux (Ubuntu) is assumed. The lecture focuses on shell scripting and the usage of sed and awk. The named tools will be used for the processing of typical file formats in bioinformatics.Assessment methodsWritten exam, complete and punctual delivery of exercises. Both count 50/50 for the gradeTeaching methodsLecture and exercises during lecture time and at homeLanguageGerman12Basics of Algorithms VOBasics of Algorithms VOLector: Ing. Christian Binder, BSc1SWS2ECTSLecture contentsIntroduction to algorithms, graph theory, basic common and bioinformatics algorithmsAssessment methodsWritten test (multiple choice)Teaching methodsSlides and practical examplesLanguageGerman-English12Transcriptomics and Genomics ILVTranscriptomics and Genomics ILVLector: FH-Prof.in Mag.a Dr.in Alexandra Graf, Lisa Tucek, BSc.2SWS4ECTSLecture contents1) Acquire knowledge on selected bioinformatics chapters (Next Generation Sequencing, ChIP-Seq, RNA-Seq) ans 2) Application of relevant bioinformatics tools for the analysis of associated data 3) using script languages under Linux, such as Bash, awk and perl.Assessment methods40 % Workshop: Hand in a short program which solved a given task 60% Lecture: Written examTeaching methods- Introductions and explanations (lecture) - Exercises using the computerLanguageGerman24Transcriptomics and Genomics Practice UETranscriptomics and Genomics Practice UELector: FH-Prof.in Mag.a Dr.in Alexandra Graf, Lisa Tucek, BSc.1.5SWS3ECTSLecture contentsThe exercise should give students the option of working with software and programs introduced in the lecture Transcriptomics and Genomics.Assessment methodsPractical examplesTeaching methodsMoodle, Linux server, example scriptsLanguageGerman1.53
2. Semester LectureSWSECTSMachine Learning Methods ILVMachine Learning Methods ILVLector: Dipl.-Ing. Dr.techn. David Steyrl1SWS2ECTSLecture contentsUpon successful completion, students will have knowledge in: - historical outline of machine-learning development - key terms of the field (AI, ML, ...) - important concepts (bias-variance trade off, cross-validation, ...) - overview of important algorithms in the field - basic programming in python - application of ML algorithms to real-world data Every lesson of this seminar consists of two parts. The first half is spent on theory, whereas the second half is used to expand the theoretical knowledge by practical exercises in Python.Assessment methodsWritten exam (multiple choice online in moodle, 30min). No aids are allowed to be used.Teaching methodsLecture + Exercise: - Lecture: online, videos on moodle - Exercise: Jupyter notebooks, solutions, online discussions of the exercisesLanguageEnglish12Selected Chapters of Bioinformatics SESelected Chapters of Bioinformatics SELector: FH-Prof.in Mag.a Dr.in Alexandra Graf, DI (FH) Anton Grünberg1SWS2ECTSLecture contentsThe seminar is meant to give students an overview over the various fields of bioinformatics. Experts from different fields will be invited to talk about their experiences and projects.Assessment methodsWritten exam at the end of the course.Teaching methodsSeminar Talks and DiscussionsLanguageGerman-English12Database Systems ILVDatabase Systems ILVLector: DI. Dr.techn. Dominik Ertl, FH-Prof. FH-Hon.Prof. Priv.-Doz. Mag. DI. DI. Dr.techn. Karl Michael Göschka1.5SWS3ECTSLecture contents- Database query with SQL - Persistency problems, database programming, cursor concept - Elaborating the requirements and needs for computer scientists that use database systems and focuse on Bio-informatics - Programming with Perl: Perl-DBI, CGI - Practical Programming tasks in small groupsAssessment methodsContinuous assessments throughout the course (grading of presentations and participation) Distance learning: Practical assignmentTeaching methodsStudent presentations followed by discussions Practical assignments in small groupsLanguageGerman1.53Medical Analysis of Genoms VOMedical Analysis of Genoms VOLector: DI Dr. Albert Kriegner, DI(FH) Dr. Stephan Pabinger1SWS2ECTSLecture contents-introduction in medical genome analysis -quality evaluation of the data -identification of variations -annotation of variations -interpretation of data -methods for "genetic testing"Assessment methods- presence and active participation - presentations done by the studentsTeaching methods- lecture with exercise - presentations done by the students and discussionLanguageGerman12Specific Statistics Practice UESpecific Statistics Practice UELector: Eva Valerie Lehner, BSc, Dipl.-Ing. Dr. Alexandra Posekany1SWS2ECTSLecture contentsGeneral topics of statistics: statistical modeling, selection of models generalized models (logistic regression) bayesian estimate and modeling creation of interactive apllicatins with R ShinyAssessment methodscontinuous assessments throughout the course and final examinationTeaching methodsApplied practice with "R"LanguageGerman12Structure Prediction in Biopolymeres VOStructure Prediction in Biopolymeres VOLector: Dr. Sven Brüschweiler, Dr. Tanja Gesell1SWS2ECTSLecture contentsThis lecture gives an introduction to basic principles of protein and RNA structure using top-down and bottom-up approaches for structure predictions. Topics include: - Introduction of experimental structure determination methods of biopolymers - Alignment methods and programs for structure prediction methods - In silicon prediction of RNA structure using dynamic programming - Genome wides screens of RNA structure - In silicon prediction of protein secondary and tertiary structure - Visualisations of RNA and Protein structuresAssessment methodsPractical sessions and a project at the end of the course (50% each).Teaching methodstheoretical and practical exercisesLanguageGerman-English12Data Analysis Laboratory LBData Analysis Laboratory LBLector: FH-Prof.in Mag.a Dr.in Alexandra Graf, Ing. DI (FH) Dr. Harald Kühnel, MSc, Katharina Seiberl, BSc MSc2SWS4ECTSLecture contentsOxford Nanopore data will be analysed by chosen appropriate methods and establishing an analysis pipeline. In the wet lab DNA will be extracted and sequenced. The created data will be analysed with the method that was established with the literature data.Assessment methodsImmanent examination, (protocol and results of analysis)Teaching methodsPresentation, practical in the wet-lab and in-silicoLanguageGerman24Master Thesis Preparation SEMaster Thesis Preparation SELector: FH-Prof.in Mag.a Dr.in Alexandra Graf0.5SWS1ECTSLecture contentsForm and features of the written thesis. Presentation techniques.Assessment methods-Teaching methodsTalk - discussionLanguageGerman-English0.51Softwaredevelopment ILVSoftwaredevelopment ILVLector: DI Christian Heiderer3SWS6ECTSLecture contentsThis course looks at the basics of programming in C++. After repeating the basic language constructs of C we will shortly focus on working with arrays, pointers and references. Switching to C++, we will learn through hands-on programming effective application of following important fields: - Usage of the C++ Standard Library: Strings and container classes/templates - Data modelling with C++ classes - Implementation of Graphical User Interfaces (GUI) using QT Additional non-C++ related targets are: - Understanding of the Software Development Process - Efficient work in small software projects - Writing easy-to-read and easy-to-maintain codeAssessment methodsSmall self-written program at end of courseTeaching methodsPresentations and discussions, hands-on practice on the PC, discussions.LanguageGerman-English36Applied Programming Practice ILVApplied Programming Practice ILVLector: Ing. Christian Binder, BSc, Msc Josef Moser, BSc3SWS6ECTSLecture contents- Introduction to programming using C/C++ with Linux - Usage of Compilers and Debuggers - Implementation of Examples - Excursus into object-oriented Progamming and high Performance computing - From the basic idea to the implementationAssessment methodsrecurring tests, exercises, presentation, collaborationTeaching methods- lecture - presentation and collaboration - practice during the lecture - exercisesLanguageGerman36
3. Semester LectureSWSECTS Automation ILV 36 Biotechnological Seminar SE 0.51 Business Plan and Cost Accounting ILV 24 Validification of Software and Medical Devices VO 12 Innovation and Entrepreneurship ILV 12 Clinical Bioinformatics ILV 1.53 Metagenom Analysis ILV 12 Molecular Design ILV 1.53 Network and Internet Technologies ILV 12 Patenting ILV 12 Computational Systems Biology ILV 1.53
4. Semester LectureSWSECTS Master Thesis Supervision AP 01 Master Thesis MT 028 Master Thesis Seminar SE 11
> FH-Prof. DI Dr. Michael Maurer Head of Degree Program Bioengineering, Bioinformatics, Biotechnological Quality Management, Bioprocess Engineering T: +43 1 606 68 77-3601michael.maurer@fh-campuswien.ac.at