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Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres

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

Fadi Alharbi¹, Aleksandar Vakanski¹

¹Department of Computer Science, University of Idaho, Moscow, ID 83844, USA.

Bioengineering (Basel, Switzerland)

|February 25, 2023

Summary

This summary is machine-generated.

Machine learning, particularly deep learning, shows promise for cancer classification by analyzing gene expression data. These methods effectively identify unique gene patterns for early cancer detection and diagnosis.

Keywords:

cancer classification gene expression analysis machine learning

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Cancer is a leading cause of death globally, necessitating advanced diagnostic tools.
Gene expression analysis, using methods like DNA microarrays and RNA-sequencing, provides crucial insights into cellular and genetic characteristics.
Early cancer detection is vital for improving patient outcomes and treatment efficacy.

Purpose of the Study:

To review recent advancements in machine learning (ML) for cancer classification based on gene expression data.
To highlight the advantages of deep learning (DL) models in identifying complex gene expression patterns indicative of various cancers.
To provide a comprehensive overview of ML approaches, data handling techniques, and future research directions in this field.

Main Methods:

Review of conventional and deep learning-based machine learning methods for gene expression analysis.
Inclusion of studies utilizing various deep neural network architectures (MLPs, CNNs, RNNs, Graph Networks, Transformers).
Examination of data collection, preprocessing, and feature engineering techniques for high-dimensional gene expression data.

Main Results:

Deep learning models demonstrate significant potential in classifying cancers by effectively analyzing intricate gene expression patterns.
Conventional ML methods and various DL architectures are applicable to gene expression data for cancer classification.
Effective data preprocessing and feature engineering are crucial for handling the high dimensionality of gene expression datasets.

Conclusions:

Machine learning, especially deep learning, offers powerful tools for cancer classification using gene expression data.
Further research is needed to explore novel ML algorithms and refine existing ones for enhanced accuracy and efficiency in cancer detection.
The integration of advanced computational methods with gene expression analysis holds promise for the future of cancer diagnostics.