Bioinformatics

VIMIAV10  |  Szabadon választható  |  Credit: 4

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.

    • Lecturers

    Antal Péter
    Péter Antal

    associate professor

    Course coordinator

    Antal Mátyás
    Mátyás Antal

    PhD student

    Bruncsics Bence
    Bence Bruncsics

    research assistant

    Marosi Márk
    Márk Marosi

    PhD student

    Pogány Domonkos
    Domonkos Pogány

    PhD student

    Sándor Dániel
    Dániel Sándor

    PhD student

    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.