Machine learning algorithms and biomarkers identification for pancreatic cancer diagnosis using multi-omics data integration

Pancreatic cancer is a lethal type of cancer with most of the cases being diagnosed in an advanced stage and poor prognosis. Developing new diagnostic and prognostic markers for pancreatic cancer can significantly improve early detection and patient outcomes. These biomarkers can potentially revolut...

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Published in:Pathology, research and practice Vol. 263; p. 155602
Main Authors: Rouzbahani, Arian Karimi, Khalili-Tanha, Ghazaleh, Rajabloo, Yasamin, Khojasteh-Leylakoohi, Fatemeh, Garjan, Hassan Shokri, Nazari, Elham, Avan, Amir
Format: Journal Article
Language:English
Published: Germany Elsevier GmbH 01.11.2024
Subjects:
Algorithms
Biomarkers
Biomarkers, Tumor - analysis
Diagnosis
Humans
Machine Learning
Multiomics
Pancreatic cancer
Pancreatic Neoplasms - diagnosis
Prognosis
Biomarkers
Prognosis
Diagnosis
Pancreatic cancer
Machine learning
ISSN:0344-0338, 1618-0631, 1618-0631
Online Access:Get full text
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Summary:Pancreatic cancer is a lethal type of cancer with most of the cases being diagnosed in an advanced stage and poor prognosis. Developing new diagnostic and prognostic markers for pancreatic cancer can significantly improve early detection and patient outcomes. These biomarkers can potentially revolutionize medical practice by enabling personalized, more effective, targeted treatments, ultimately improving patient outcomes. The search strategy was developed following PRISMA guidelines. A comprehensive search was performed across four electronic databases: PubMed, Scopus, EMBASE, and Web of Science, covering all English publications up to September 2022. The Newcastle-Ottawa Scale (NOS) was utilized to assess bias, categorizing studies as "good," "fair," or "poor" quality based on their NOS scores. Descriptive statistics for all included studies were compiled and reviewed, along with the NOS scores for each study to indicate their quality assessment. Our results showed that SVM and RF are the most widely used algorithms in machine learning and data analysis, particularly for biomarker identification. SVM, a supervised learning algorithm, is employed for both classification and regression by mapping data points in high-dimensional space to identify the optimal separating hyperplane between classes. The application of machine-learning algorithms in the search for novel biomarkers in pancreatic cancer represents a significant advancement in the field. By harnessing the power of artificial intelligence, researchers are poised to make strides towards earlier detection and more effective treatment, ultimately improving patient outcomes in this challenging disease
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ISSN:0344-0338
1618-0631
1618-0631
DOI:10.1016/j.prp.2024.155602