# Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review

arXiv: https://arxiv.org/abs/2301.12222 · HTML: https://mlsanity.com/paper/2301.12222/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 156 references that are in the map.
- First posted on arXiv 28 Jan 2023.

## Builds on (most cited first, 8 of 8)

- [CGC-Net: Cell Graph Convolutional Network for Grading of Colorectal Cancer Histology Images](https://mlsanity.com/paper/1909.01068/) (2019)
- [Deep-Learning for Classification of Colorectal Polyps on Whole-Slide Images](https://mlsanity.com/paper/1703.01550/) (2017)
- [Convolutional neural network models for cancer type prediction based on gene expression](https://mlsanity.com/paper/1906.07794/) (2019)
- [A graph-embedded deep feedforward network for disease outcome classification and feature selection using gene expression data](https://mlsanity.com/paper/1801.06202/) (2018)
- [ViT-ReT: Vision and Recurrent Transformer Neural Networks for Human Activity Recognition in Videos](https://mlsanity.com/paper/2208.07929/) (2022)
- [Hybrid gene selection approach using XGBoost and multi-objective genetic algorithm for cancer classification](https://mlsanity.com/paper/2106.05841/) (2021)
- [A Deep Embedded Refined Clustering Approach for Breast Cancer Distinction based on DNA Methylation](https://mlsanity.com/paper/2102.09563/) (2021)
- [A comprehensive survey on computational learning methods for analysis of gene expression data](https://mlsanity.com/paper/2202.02958/) (2022)

## Built on by (most cited first, 7 of 7)

- [Precision Cancer Classification and Biomarker Identification from mRNA Gene Expression via Dimensionality Reduction and Explainable AI](https://mlsanity.com/paper/2410.07260/) (2024)
- [Pan-cancer gene set discovery via scRNA-seq for optimal deep learning based downstream tasks](https://mlsanity.com/paper/2408.07233/) (2024)
- [LASSO-MOGAT: A Multi-Omics Graph Attention Framework for Cancer Classification](https://mlsanity.com/paper/2408.17384/) (2024)
- [Self-supervised learning on gene expression data](https://mlsanity.com/paper/2507.13912/) (2025)
- [A Comparative Analysis of Gene Expression Profiling by Statistical and Machine Learning Approaches](https://mlsanity.com/paper/2402.00926/) (2024)
- [Comparative Analysis of Multi-Omics Integration Using Advanced Graph Neural Networks for Cancer Classification](https://mlsanity.com/paper/2410.05325/) (2024)
- [Transformer-Based Representation Learning for Robust Gene Expression Modeling and Cancer Prognosis](https://mlsanity.com/paper/2504.09704/) (2025)

## Abstract (arXiv)

Cancer is a term that denotes a group of diseases caused by abnormal growth of cells that can spread in different parts of the body. According to the World Health Organization (WHO), cancer is the second major cause of death after cardiovascular diseases. Gene expression can play a fundamental role in the early detection of cancer, as it is indicative of the biochemical processes in tissue and cells, as well as the genetic characteristics of an organism. Deoxyribonucleic Acid (DNA) microarrays and Ribonucleic Acid (RNA)- sequencing methods for gene expression data allow quantifying the expression levels of genes and produce valuable data for computational analysis. This study reviews recent progress in gene expression analysis for cancer classification using machine learning methods. Both conventional and deep learning-based approaches are reviewed, with an emphasis on the ap-plication of deep learning models due to their comparative advantages for identifying gene patterns that are distinctive for various types of cancers. Relevant works that employ the most commonly used deep neural network architectures are covered, including multi-layer perceptrons, convolutional, recurrent, graph, and transformer networks. This survey also presents an overview of the data collection methods for gene expression analysis and lists important datasets that are commonly used for supervised machine learning for this task. Furthermore, reviewed are pertinent techniques for feature engineering and data preprocessing that are typically used to handle the high dimensionality of gene expression data, caused by a large number of genes present in data samples. The paper concludes with a discussion of future research directions for machine learning-based gene expression analysis for cancer classification.