---
title: "Machine learning applications in genetics and genomics"
authors: ["Libbrecht MW", "Noble WS"]
year: 2015
journal: Nature Reviews Genetics
type: review
tags: [machine-learning, genomics, computational-biology]
cited_by: 2800
impact: foundational
---

# [[Machine Learning]] [[Applications]] in genetics and genomics

## Summary

Survey of [[Machine]] [[Learning]] methods for genomic [[Data Analysis]]. Covers supervised, unsupervised, and [[Deep Learning]] approaches with practical considerations.

## [[Key Methods]]

- **Supervised**: [[Random Forests]] for variant classification
- **Unsupervised**: [[Dimensionality Reduction]] for expression [[Data]]
- **[[Deep]] Learning**: [[Convolutional Neural Networks]] for sequence motifs

## Methodology

[[Feature Engineering]] for genomic data. [[Cross-Validation]] strategies for genomic datasets with linkage disequilibrium.

## Relevance to My Work

Methods [[Guide]] for [[Drug-Target Prediction]] [[Project]]. [[Random Forests]] [[Used In]] [[Experiment-2024-11-20]].

## Applications

- [[Variant Effect Prediction]]
- [[Gene Expression Classification]]
- [[Regulatory Element Discovery]]
