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    Bioinformatics

    A tantárgy neve magyarul / Name of the subject in Hungarian: Bioinformatika

    Last updated: 2022. május 3.

    Budapest University of Technology and Economics
    Faculty of Electrical Engineering and Informatics
    Electrical Engineering
    Informatics Engineering
    Free choice subject
    Course ID Semester Assessment Credit Tantárgyfélév
    VIMIAV10   4/0/0/v 4  
    3. Course coordinator and department Dr. Antal Péter,
    4. Instructors Péter Antal, Bence Bolgár, András Gézsi, Péter Sárközy
    5. Required knowledge Probability Theory
    6. Pre-requisites
    Kötelező:
    NEM ( TárgyEredmény( "BMEVIMIM201" , "jegy" , _ ) >= 2
    VAGY
    TárgyEredmény("BMEVIMIM201", "FELVETEL", AktualisFelev()) > 0)

    A fenti forma a Neptun sajátja, ezen technikai okokból nem változtattunk.

    A kötelező előtanulmányi rend az adott szak honlapján és képzési programjában található.

    7. Objectives, learning outcomes and obtained knowledge

    The novel measurement technologies in molecular biology has revolutionized life sciences and led to the emergence of data-driven, hypothesis-free research paradigm. The course introduces the informatics and statistical aspects of bioinformatics through key healthcare and pharmaceutical issues: medical decision support in diagnostic and therapy recommendation, the integrated analysis of genetic and genomic data, drug target prediction methods.

    The main data science concepts and methods demonstrated in this course are the following:

    • Statistical inference paradigms. The multiple hypothesis testing problem. Methods for enrichment analysis.
    • Methods of dimensionality reduction, with a primary focus on methods using expert knowledge in the form of ontologies.
    • Clustering algorithms, especially methods which are applicable to multiple similarity matrices.
    • High dimensionality prediction methods capable of handling heterogeneous representations, such as multiple kernel learning.
    • Network theory, the structural properties of molecular interaction networks and network diffusion methods.
    • Probabilistic graphical models and their inference and learning algorithms.
    • Causal inference paradigms.
    • Text mining methods in bioinformatics.
    • Graph databases and semantic technologies (drug knowledge bases, gene ontologies, disease code systems).

    The theory will be demonstrated in the following real-world applications:

    • Biomarker-based tumor diagnostics.
    • The genetic background of common diseases.
    • Co-occurrence and comorbidity networks.
    • Examining the genetic background of healthy aging across multiple species.
    • Drug target prediction
    • Side effect and novel indication prediction of drugs and drug combinations.
    8. Synopsis
    • Medical decision support in oncology: the use of decision networks in diagnostics and therapy selection.
    • Genetic measurement technology: genotyping, sequencing, data processing, variant calling and imputation.
    • The statistical analysis of genome wide association data in psychiatry: data preparation, univariate and multivariate prediction methods, enrichment analysis, network analysis methods.
    • The analysis of genome wide association data in aging: the special requirements of handling rare mutations.
    • Statistical analysis of genome wide gene expression data in immunology: network methods
    • The analysis of disease and gene networks in medical biology and pharmaceutical research.
    • Analysis of everyday lifestyle data including data from wearable sensors: time-series data analysis.
    • Causal inference in aging research with genetic knock-out experiments.
    • Methods of biomarker analysis.
    • Planned data collection and study design.
    • Text mining methods in bioinformatics.
    • The role of semantic technologies in bio- and chemoinformatics.
    • The phases of pharmaceutical research, methods for drug-target prediction.
    • Recommendation systems in bioinformatics and pharmaceutical research.
    9. Method of instruction The theory part of the course will be presented as a series of lectures, independent work is encouraged through homework assignments.
    10. Assessment

    1. Lecturing interval: Submission and acceptance of the homework assignment by the end of the lecturing interval.

    2. Examination interval: Oral examination, acceptance of the homework assignment is mandatory for oral examination.

    3. Grading: The final grade is received on the oral examination.

    11. Recaps Homework assignments may be submitted by the end of the retake week.
    12. Consultations As required, arranged on demand with the lecturers.
    13. References, textbooks and resources

    Antal Péter - Arany Ádám - Bolgár Bence - Gézsi András - Hajós Gergely - Hullám Gábor - Marx Péter - Millinghoffer András - Poppe László - Sárközy Péter:  Bioinformatics
     ISBN-13 978-963-2791-79-1, Typotex, 2014

    14. Required learning hours and assignment
    Kontakt óra56
    Félévközi készülés órákra14
    Felkészülés zárthelyire 
    Házi feladat elkészítése20
    Kijelölt írásos tananyag elsajátítása 
    Vizsgafelkészülés30
    Összesen120
    15. Syllabus prepared by

    Name:

    Classification:

    Department:

    Dr. Péter Antal

    associate professor

    Department of Measurement and Information Systems

    Comments The title of the subject in Hungarian: Bioinformatika.