Big Data, Genes, and Medicine

Por: Coursera . en: , ,

  • Genes and Data
    • After this module, you will be able to
      1. Locate and download files for data analysis involving genes and medicine.
      2. Open files and preprocess data using R language.
      3. Write R scripts to replace missing values, normalize data, discretize data, and sample data.
  • Preparing Datasets for Analysis
    • After this module, you will be able to:
      1. Locate and download files for data analysis involving genes and medicine.
      2. Open files and preprocess data using R language.
      3. Write R scripts to replace missing values, normalize data, discretize data, and sample data.
  • Finding Differentially Expressed Genes
    • After this module, you will be able to
      1. Select features from highly dimensional datasets.
      2. Evaluate the performance of feature selection methods.
      3. Write R scripts to select features from datasets involving gene expressions.
  • Predicting Diseases from Genes
    • After this module, you will be able to
      1. Build classification and prediction models.
      2. Evaluate the performance of classification and prediction methods.
      3. Write R scripts to classify and predict diseases from gene expressions.
  • Determining Gene Alterations
    • After this module, you will be able to
      1. List different types of gene alterations.
      2. Compare and contrast methods for detecting gene mutations.
      3. Compare and contrast methods for detecting methylation.
      4. Compare and contrast methods for detecting copy number variations.
      5. Quantify genomic alterations.
      6. Connect genomic alterations to differential expression of genes.
      7. Write programs in R for determining gene alterations and their relationship with gene expression.
  • Clustering and Pathway Analysis
    • After this module, you will be able to 1. Find clusters in biomedical data involving genes.2. Analyze and visualize biological pathways. 3. Write R scripts for clustering and for pathway analysis.

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