# A comprehensive survey on computational learning methods for analysis of gene expression data

arXiv: https://arxiv.org/abs/2202.02958 · HTML: https://mlsanity.com/paper/2202.02958/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 320 references that are in the map.
- First posted on arXiv 7 Feb 2022.

## Builds on (most cited first, 9 of 9)

- [Multimodal Machine Learning: A Survey and Taxonomy](https://mlsanity.com/paper/1705.09406/) (2017)
- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [Deep Reinforcement Learning: An Overview](https://mlsanity.com/paper/1701.07274/) (2017)
- [What do we need to build explainable AI systems for the medical domain?](https://mlsanity.com/paper/1712.09923/) (2017)
- [MLlib: Machine Learning in Apache Spark](https://mlsanity.com/paper/1505.06807/) (2015)
- [Attention based convolutional neural network for predicting RNA-protein binding sites](https://mlsanity.com/paper/1712.02270/) (2017)
- [Comparison of machine learning and deep learning techniques in promoter prediction across diverse species](https://mlsanity.com/paper/2105.07659/) (2021)
- [Unsupervised Gene Expression Data using Enhanced Clustering Method](https://mlsanity.com/paper/1307.3337/) (2013)
- [Deep Reinforcement Learning: An Overview](https://mlsanity.com/paper/1806.08894/) (2018)

## Built on by (most cited first, 1 of 1)

- [Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review](https://mlsanity.com/paper/2301.12222/) (2023)

## Abstract (arXiv)

Computational analysis methods including machine learning have a significant impact in the fields of genomics and medicine. High-throughput gene expression analysis methods such as microarray technology and RNA sequencing produce enormous amounts of data. Traditionally, statistical methods are used for comparative analysis of gene expression data. However, more complex analysis for classification of sample observations, or discovery of feature genes requires sophisticated computational approaches. In this review, we compile various statistical and computational tools used in analysis of expression microarray data. Even though the methods are discussed in the context of expression microarrays, they can also be applied for the analysis of RNA sequencing and quantitative proteomics datasets. We discuss the types of missing values, and the methods and approaches usually employed in their imputation. We also discuss methods of data normalization, feature selection, and feature extraction. Lastly, methods of classification and class discovery along with their evaluation parameters are described in detail. We believe that this detailed review will help the users to select appropriate methods for preprocessing and analysis of their data based on the expected outcome.