# Machine Learning for Genomic Data

arXiv: https://arxiv.org/abs/2111.08507 · HTML: https://mlsanity.com/paper/2111.08507/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
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- First posted on arXiv 15 Nov 2021.

## Abstract (arXiv)

This report explores the application of machine learning techniques on short timeseries gene expression data. Although standard machine learning algorithms work well on longer time-series', they often fail to find meaningful insights from fewer timepoints. In this report, we explore model-based clustering techniques. We combine popular unsupervised learning techniques like K-Means, Gaussian Mixture Models, Bayesian Networks, Hidden Markov Models with the well-known Expectation Maximization algorithm. K-Means and Gaussian Mixture Models are fairly standard, while Hidden Markov Model and Bayesian Networks clustering are more novel ideas that suit time-series gene expression data.