Chapter 3 Introduction

Each chapter of the book is composed of the following three aspects:

  1. An important topic or question in translational bioinformatics

  2. An example of publicly available data (if data is not available publicly, only the methodologies will be described)

  3. R code utilizing advanced statistics, machine learning and/or causal inference methods

3.1 Topics

The following topics will be covered:

  • Dimensionality reduction

    • PCA and tSNE
    • Feature selection and extraction for machine learning

  • Prognostic and Predictive biomarkers

    • Predictive biomarker identification using interpretative machine learning approach.

    • Prognostic predictions using Bayesian statistics.

  • Benchmarking survival predictions using statistics and machine learning

  • Subgroup identification using causal discovery and counterfactual modeling

  • Estimand thinking in biomarkers:

    • ICH E9 addendum on Estimands

    • Estimands thinking and biomarkers