Background: The adoption of biomarkers as part of high-throughput, complex
microarray or sequencing data has necessitated the discovery and validation of these data
through machine learning. Machine learning has remained a fundamental and indispensable
tool due to its efficacy and efficiency in both feature extraction of relevant biomarkers
as well as the classification of samples as validation of the discovered biomarkers.
Objectives: This review aims to present the impact and ability of various machine learning
methodologies and models to process high-throughput, high-dimensionality data
found within mass spectrometry, microarray, and DNA/RNA-sequence data; data that
precluded biomarker discovery prior to the use of machine learning.
Methods: A vast array of literature highlighting machine learning for biomarker discovery
was reviewed, resulting in the eligibility of 21 machine learning algorithms/networks
and 3 combinatory architectures, spanning 17 fields of study. This literature was
screened to investigate the usage and development of machine learning within the framework
of biomarker discovery.
Results: Out of the 93 papers collected, a total of 62 biomarker studies were further reviewed
across different subfields-49 of which employed machine learning algorithms,
and 13 of which employed neural network-based models. Through the application, innovation,
and creation of tools in biomarker-related machine learning methodologies, its
use allowed for the discovery, accumulation, validation, and interpretation of biomarkers
within varied data formats, sources, as well as fields of study.
Conclusion: The use of machine learning methodologies for biomarker discovery is critical
to the analysis of various types of data used for biomarker discovery, such as mass
spectrometry, nucleotide and protein sequencing, and image (e.g. CT-scan) data. Further
studies containing more standardized techniques for evaluation, and the use of cutting-
edge machine learning architectures may lead to more accurate and specific results.