ArticleFrontiers in cell and developmental biology2020
Early Diagnosis of Pancreatic Ductal Adenocarcinoma by Combining Relative Expression Orderings With Machine-Learning Method.
Article in Frontiers in cell and developmental biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Enhancing tumor T cell antigen prediction by integrating deep protein representations.BMC bioinformatics · 2026Article
- Advancements in Radiomics-Based AI for Pancreatic Ductal Adenocarcinoma.Bioengineering (Basel, Switzerland) · 2025Review
- Predicting pathological response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer with two step feature selection and ensemble learning.Scientific reports · 2025Article
- Clinical significance of risk factor analysis in pancreatic cancer by using supervised model of machine learning.Frontiers in medicine · 2025Article
- Development of machine learning-based predictors for early diagnosis of hepatocellular carcinoma.Scientific reports · 2024Article
- Promoter Prediction in Agrobacterium tumefaciens Strain C58 by Using Artificial Intelligence Strategies.Methods in molecular biology (Clifton, N.J.) · 2024Article
- The expression profile of Gasdermin C-related genes predicts the prognosis and immunotherapy response of pancreatic adenocarcinoma.American journal of cancer research · 2023Article
- Computational prediction of promotors inFrontiers in microbiology · 2023Article
- Prediction of apoptosis protein subcellular location based on amphiphilic pseudo amino acid composition.Frontiers in genetics · 2023Article
- Empirical comparison and recent advances of computational prediction of hormone binding proteins using machine learning methods.Computational and structural biotechnology journal · 2023Review
- Identification of cancer risk assessment signature in patients with chronic obstructive pulmonary disease and exploration of the potential key genes.Annals of medicine · 2022Article
- Epigenetics in Pancreatic Ductal Adenocarcinoma: Impact on Biology and Utilization in Diagnostics and Treatment.Cancers · 2022Review
- Early diagnosis of pancreatic cancer: What strategies to avoid a foretold catastrophe.World journal of gastroenterology · 2022Review
- Application of Artificial Intelligence in the Management of Pancreatic Cystic Lesions.Biomimetics (Basel, Switzerland) · 2022Review
- iThermo: A Sequence-Based Model for Identifying Thermophilic Proteins Using a Multi-Feature Fusion Strategy.Frontiers in microbiology · 2022Article
- Research trends of artificial intelligence in pancreatic cancer: a bibliometric analysis.Frontiers in oncology · 2022Article
- Machine learning-featured Secretogranin V is a circulating diagnostic biomarker for pancreatic adenocarcinomas associated with adipopenia.Frontiers in oncology · 2022Article
- Comprehensive Analysis of the Molecular Characteristics and Prognosis value of AT II-associated Genes in Non-small Cell Lung Cancer.Computational and mathematical methods in medicine · 2022Article
- Recent advances in artificial intelligence for pancreatic ductal adenocarcinoma.World journal of gastroenterology · 2021Review
- iTTCA-RF: a random forest predictor for tumor T cell antigens.Journal of translational medicine · 2021Article
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6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is an aggressive and lethal cancer deeply affecting human health. Diagnosing early-stage PDAC is the key point to PDAC patients' survival. However, the biomarkers for diagnosing early PDAC are inexact in most cases. Therefore, it is highly desirable to identify an effective PDAC diagnostic biomarker. In the current work, we designed a novel computational approach based on within-sample relative expression orderings (REOs). A feature selection technique called minimum redundancy maximum relevance was used to pick out optimal REOs. We then compared the performances of different classification algorithms for discriminating PDAC and its adjacent normal tissues from non-PDAC tissues. The support vector machine algorithm is the best one for identifying early PDAC diagnostic biomarker. At first, a signature composed of nine gene pairs was acquired from microarray gene expression data sets. These gene pairs could produce satisfactory classification accuracy up to 97.53% in fivefold cross-validation. Subsequently, two types of data from diverse platforms, namely, microarray and RNA-Seq, were used to validate this signature. For microarray data, all (100.00%) of 115 PDAC tissues and all (100.00%) of 31 PDAC adjacent normal tissues were correctly recognized as PDAC. In addition, 88.24% of 17 non-PDAC (normal or pancreatitis) tissues were correctly classified. For the RNA-Seq data, all (100.00%) of 177 PDAC tissues and all (100.00%) of 4 PDAC adjacent normal tissues were correctly recognized as PDAC. Validation results demonstrated that the signature had a good cross-platform effect for early detection of PDAC. This work developed a new robust signature that might be a promising biomarker for early PDAC diagnosis.
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