{"id":9212,"date":"2023-05-15T12:33:51","date_gmt":"2023-05-15T10:33:51","guid":{"rendered":"https:\/\/semmelweis.hu\/bioinformatika\/?page_id=9212"},"modified":"2023-06-23T09:59:34","modified_gmt":"2023-06-23T07:59:34","slug":"detailed-schedule","status":"publish","type":"page","link":"https:\/\/semmelweis.hu\/bioinformatika\/english-2\/clinical-bioinformatics\/detailed-schedule\/","title":{"rendered":"Detailed schedule"},"content":{"rendered":"

The topics given in the thematic guide are 15-minute lectures.<\/p>\n

Theory:<\/u><\/strong><\/p>\n

I. blokk: Basics of Bioinformatics<\/strong><\/p>\n

    \n
  1. Introduction to bioinformatics ( Dr. Bal\u00e1zs Gy\u0151rffy)<\/li>\n
  2. Utilization of a training and test set (Dr. J\u00e1nos Tibor Fekete)<\/li>\n
  3. Statistical errors and dichotomania (Dr. J\u00e1nos Tibor Fekete)<\/li>\n
  4. Survival analysis: Cox regression and the Kaplan-Meier plot (Prof. Dr. Bal\u00e1zs Gy\u0151rffy)<\/li>\n
  5. ROC analysis: predicting sensitivity and specificity (Dr. J\u00e1nos Tibor Fekete)<\/li>\n
  6. Correlation (Dr. Ot\u00edlia Menyh\u00e1rt)<\/li>\n
  7. Multiple hypothesis testing (Prof. Dr. Bal\u00e1zs Gy\u0151rffy)<\/li>\n
  8. Applications of bioinformatics and artificial intelligence (Prof. Dr. Bal\u00e1zs Gy\u0151rffy)<\/li>\n<\/ol>\n

    Block I theoretical hours: 2,7 hours (120 minutes)<\/em><\/p>\n

    II. blokk: Omics<\/strong><\/p>\n

      \n
    1. Introduction to genomics (Prof. Dr. Bal\u00e1zs Gy\u0151rffy)<\/li>\n
    2. Similar genes and proteins, BLAST (Prof. Dr. Bal\u00e1zs Gy\u0151rffy)<\/li>\n
    3. Genomics: quality control (Dr. Gy\u00f6ngyi Munk\u00e1csy)<\/li>\n
    4. Genomics: alignment of data to a reference genome (Dr. Gy\u00f6ngyi Munk\u00e1csy)<\/li>\n
    5. Genomics: identifying mutations (SNV, indels) in normal and tumor samples (Dr. Gy\u00f6ngyi Munk\u00e1csy)<\/li>\n
    6. Genomics: determining the consequence of a mutation (Dr. Gy\u00f6ngyi Munk\u00e1csy)<\/li>\n
    7. Genomics: what is the clinical relevance of a mutation, ClinVar, dbSNP (Dr. Gy\u00f6ngyi Munk\u00e1csy)<\/li>\n
    8. Genomics: copy number variations (Dr. Gy\u00f6ngyi Munk\u00e1csy)<\/li>\n
    9. Genomics: identifying processing artefacts and quality issues (Dr. Ot\u00edlia Menyh\u00e1rt)<\/li>\n
    10. Transcriptomics: processing RNA-seq data (Prof. Dr. Bal\u00e1zs Gy\u0151rffy)<\/li>\n
    11. Processing single-cell data (Prof. Dr. Bal\u00e1zs Gy\u0151rffy)<\/li>\n
    12. Genomics: GeneBank and the database of genes (Dr. Attila Marcell Sz\u00e1sz)<\/li>\n<\/ol>\n

      Block II theoretical hours: 4 hours (180 minutes)<\/em><\/p>\n

      III. blokk: Proteomics<\/strong><\/p>\n

        \n
      1. Proteomics and transcriptomics: pre-processing (Prof. Dr. Bal\u00e1zs Gy\u0151rffy)<\/li>\n
      2. Proteomics: tools to analyze immunhistochemistry results (Dr. Gy\u00f6ngyi Munk\u00e1csy)<\/li>\n
      3. Proteomics: processing mass spectrometry (Dr. \u00c1ron Bartha)<\/li>\n
      4. Proteomics: advantages and limitations of mass spectrometry (Dr. \u00c1ron Bartha)<\/li>\n
      5. Protein structure determination: physical\/chemical methods (Dr. G\u00e1bor Tusn\u00e1dy)<\/li>\n
      6. Modelling protein structure: from simple (homology model) to complex (ab initio) (Dr. G\u00e1bor Tusn\u00e1dy)<\/li>\n
      7. Modelling the structure of transmembrane proteins (Dr. G\u00e1bor Tusn\u00e1dy)<\/li>\n
      8. Proteomics: understanding molecular functions, Uniprot (Dr. Gy\u00f6ngyi Munk\u00e1csy)<\/li>\n
      9. Signal transduction, KEGG (Dr. Attila Marcell Sz\u00e1sz)<\/li>\n
      10. Graphical comparison of two groups: boxplot, violin plot, density plot, heatmap, correlation, matrix (Dr. \u00c1ron Bartha)<\/li>\n<\/ol>\n

        Block III theoretical hours: 3.3 hours (150 minutes)<\/em><\/p>\n

        IV. blokk: Artificial intelligence<\/strong><\/p>\n

          \n
        1. Machine learning (Dr. J\u00e1nos Tibor Fekete)<\/li>\n
        2. The Bayes rule (Dr. J\u00e1nos Tibor Fekete)<\/li>\n
        3. Principal component analysis (Dr. \u00c1ron Bartha)<\/li>\n
        4. Determining distance (Prof. Dr. Bal\u00e1zs Gy\u0151rffy)<\/li>\n
        5. Clustering (Prof. Dr. Bal\u00e1zs Gy\u0151rffy)<\/li>\n
        6. Neural networks (Prof. Dr. Bal\u00e1zs Gy\u0151rffy)<\/li>\n
        7. Convolutional neural networks (CNN) (Dr. Ot\u00edlia Menyh\u00e1rt)<\/li>\n
        8. Deep learning (Dr. Ot\u00edlia Menyh\u00e1rt)<\/li>\n
        9. Clinical application of a decision tree (Dr. \u00c1ron Bartha)<\/li>\n
        10. Feature selection (Dr. Ot\u00edlia Menyh\u00e1rt)<\/li>\n
        11. Support Vector Machines (Dr. J\u00e1nos Tibor Fekete)<\/li>\n
        12. Regression (Dr. Ot\u00edlia Menyh\u00e1rt)<\/li>\n
        13. The general pre-trained transformer (Dr. J\u00e1nos Tibor Fekete)<\/li>\n<\/ol>\n

          Block IV theoretical hours: 4.3 hours (195 minutes)<\/em><\/p>\n

          V. blokk: Integrative sciences<\/strong><\/p>\n

            \n
          1. Multi-omics (Dr. Ot\u00edlia Menyh\u00e1rt)<\/li>\n
          2. Gene ontology (Dr. J\u00e1nos Tibor Fekete)<\/li>\n
          3. Reproducibility issues in medical research (Dr. Ot\u00edlia Menyh\u00e1rt)<\/li>\n
          4. Evaluation of Chip-seq and ATAC-seq data (Dr. B\u00e1lint B\u00e1lint)<\/li>\n
          5. Evaluation of DNA methylation data (Dr. B\u00e1lint B\u00e1lint)<\/li>\n
          6. Epigenetic databases (Dr. B\u00e1lint B\u00e1lint)<\/li>\n
          7. Using Excel for database management (Dr. Gy\u00f6ngyi Munk\u00e1csy)<\/li>\n
          8. Data mining: Excel, PubMed, Watson (Dr. Gy\u00f6ngyi Munk\u00e1csy)<\/li>\n
          9. Using REDcap (Dr. Attila Marcell Sz\u00e1sz)<\/li>\n
          10. Time distortion and computer addiction (Dr. Ot\u00edlia Menyh\u00e1rt)<\/li>\n
          11. Blockchain and data security (Dr. \u00c1ron Bartha)<\/li>\n<\/ol>\n

            Block V theoretical hours: 3.7 hours (165 minutes)<\/em><\/p>\n

            \u00a0<\/em><\/p>\n

            Practical training:<\/u><\/strong><\/p>\n

            I. blokk: Basics of Bioinformatics<\/strong><\/p>\n

              \n
            1. Survival analysis: Cox regression and the Kaplan-Meier plot, 2 hours ( Dr. Bal\u00e1zs Gy\u0151rffy)<\/li>\n
            2. ROC analysis, 2 hours (Dr. J\u00e1nos Tibor Fekete)<\/li>\n
            3. Multiple hypothesis testing, 2 hours (Prof. Dr. Bal\u00e1zs Gy\u0151rffy)<\/li>\n
            4. Introduction to Galaxy, 1 hour (Dr. Ot\u00edlia Menyh\u00e1rt)<\/li>\n<\/ol>\n

              Block I Practical hours: 7 hours (315 minutes)<\/em><\/p>\n

              II<\/strong>. <\/strong><\/em>blokk: Genomics and transcriptomics<\/strong><\/p>\n

                \n
              1. Quality control, 2 hours (Dr. Gy\u00f6ngyi Munk\u00e1csy)<\/li>\n
              2. Alignment of data to a reference genome, 2 hours (Dr. Gy\u00f6ngyi Munk\u00e1csy)<\/li>\n
              3. Identifying variants, 2 hours (Szonja Anna Kov\u00e1cs)<\/li>\n
              4. Consequences of mutations, 2 hours (Dr. Gy\u00f6ngyi Munk\u00e1csy)<\/li>\n
              5. Transcriptomics: processing RNA-seq data, 2 hours (Prof. Dr. Bal\u00e1zs Gy\u0151rffy)<\/li>\n<\/ol>\n

                Block II Practical hours: 10 hours (450 minutes)<\/em><\/p>\n

                 <\/p>\n

                IV. blokk: Artificial intelligence<\/strong><\/p>\n

                  \n
                1. Clustering, 1 hour (Dr. Ot\u00edlia Menyh\u00e1rt)<\/li>\n
                2. Classification, 2 hours (Dr. J\u00e1nos Tibor Fekete)<\/li>\n
                3. Regression, 1 hour, (Dr. Ot\u00edlia Menyh\u00e1rt)<\/li>\n
                4. Deep learning, 2 hours (Dr. J\u00e1nos Tibor Fekete)<\/li>\n<\/ol>\n

                  Block IV Practical hours: 6 hours (270 minutes)<\/em><\/p>\n

                   <\/p>\n

                  V. blokk: Integrative science<\/strong><\/p>\n

                    \n
                  1. Epigenetics, 1 hour (Dr. B\u00e1lint B\u00e1lint)<\/li>\n<\/ol>\n

                    Block V Practical hours: 1 hour (45 minutes)<\/em><\/p>\n

                     <\/p>\n

                    A consultation is possible during the last practical session.<\/p>\n

                    Total number of theoretical hours: 18 hours (810 minutes)<\/p>\n

                    Total number of practical hours: 24 hours (1080 minutes)<\/p>\n

                     <\/p>\n

                    A t\u00f6mb\u00f6s\u00edtett napi beoszt\u00e1s tematikai eloszt\u00e1ssal (t\u00e9masz\u00e1mok a fenti lista alapj\u00e1n):<\/p>\n\n\n\n\n\n
                    \n

                    Day<\/p>\n<\/td>\n

                    		\n

                    Theory covered<\/p>\n<\/td>\n

                    		\n

                    Practical topics<\/p>\n<\/td>\n

                    		\n

                    Total hours<\/p>\n<\/td>\n<\/tr>\n

                    \n

                    1<\/p>\n<\/td>\n

                    		\n

                    1-8<\/p>\n<\/td>\n

                    		\n

                    55, 56, 57<\/p>\n<\/td>\n

                    		\n

                    8.7<\/p>\n<\/td>\n<\/tr>\n

                    \n

                    2<\/p>\n<\/td>\n

                    		\n

                    9-14, 28-30<\/p>\n<\/td>\n

                    		\n

                    58, 59, 60<\/p>\n<\/td>\n

                    		\n

                    8.0<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n","protected":false},"excerpt":{"rendered":"

                    The topics given in the thematic guide are 15-minute lectures. Theory: I. blokk: Basics of Bioinformatics Introduction to bioinformatics ( Dr. Bal\u00e1zs Gy\u0151rffy) Utilization of a training and test set (Dr. J\u00e1nos Tibor Fekete) Statistical errors and dichotomania (Dr. J\u00e1nos Tibor Fekete) Survival analysis: Cox regression and the Kaplan-Meier plot (Prof. Dr. Bal\u00e1zs Gy\u0151rffy) ROC …<\/p>\n","protected":false},"author":102018,"featured_media":0,"parent":9194,"menu_order":1,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"categories":[],"tags":[],"class_list":["post-9212","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/semmelweis.hu\/bioinformatika\/wp-json\/wp\/v2\/pages\/9212","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/semmelweis.hu\/bioinformatika\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/semmelweis.hu\/bioinformatika\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/semmelweis.hu\/bioinformatika\/wp-json\/wp\/v2\/users\/102018"}],"replies":[{"embeddable":true,"href":"https:\/\/semmelweis.hu\/bioinformatika\/wp-json\/wp\/v2\/comments?post=9212"}],"version-history":[{"count":3,"href":"https:\/\/semmelweis.hu\/bioinformatika\/wp-json\/wp\/v2\/pages\/9212\/revisions"}],"predecessor-version":[{"id":9217,"href":"https:\/\/semmelweis.hu\/bioinformatika\/wp-json\/wp\/v2\/pages\/9212\/revisions\/9217"}],"up":[{"embeddable":true,"href":"https:\/\/semmelweis.hu\/bioinformatika\/wp-json\/wp\/v2\/pages\/9194"}],"wp:attachment":[{"href":"https:\/\/semmelweis.hu\/bioinformatika\/wp-json\/wp\/v2\/media?parent=9212"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/semmelweis.hu\/bioinformatika\/wp-json\/wp\/v2\/categories?post=9212"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/semmelweis.hu\/bioinformatika\/wp-json\/wp\/v2\/tags?post=9212"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}