Machine Learning Omics

Genomics, Proteomics, Transcriptomics & Metabolomics · Part 5

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Table of Contents

  1. 1.19.1 Supervised Learning for Omics
  2. 2.19.2 Unsupervised Learning & Dimensionality Reduction
  3. 3.19.3 Feature Selection & Regularization
  4. 4.19.4 Cross-Validation & Model Evaluation
  5. 5.19.5 Deep Learning for Omics
  6. 6.19.6 Overfitting, Regularization & Benchmarking
  7. 7.Support Vector Machines (SVMs)
  8. 8.Random Forests
  9. 9.Neural Networks
  10. 10.Comparison of Supervised Methods for Omics

Key Equations

$$\min_{\mathbf{w}, b, \boldsymbol{\xi}} \; \frac{1}{2} \|\mathbf{w}\|^2 + C \sum_{i=1}^{n} \xi_i$$
$$\text{softmax}(z_k) = \frac{e^{z_k}}{\sum_{j=1}^{K} e^{z_j}}, \quad k = 1, \dots, K$$
$$\Sigma = \frac{1}{n-1} X^\top X = V \Lambda V^\top$$
$$\min_{\boldsymbol{\beta}} \; \|\mathbf{y} - X\boldsymbol{\beta}\|_2^2 + \lambda_1 \|\boldsymbol{\beta}\|_1 + \lambda_2 \|\boldsymbol{\beta}\|_2^2$$

Equations are rendered with MathJax in the PDF with professional LaTeX typesetting.

Course Context

This PDF is part of the Genomics, Proteomics, Transcriptomics & Metabolomics course on CoursesHub.World. Study genomics, proteomics, transcriptomics and metabolomics with 20 free chapters covering DNA sequencing, RNA-Seq, mass spectrometry, metabolic profiling, multi-omics integration, and precision medi...

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