ArticleComputational intelligence and neuroscience2022
Quantitative Detection of Gastrointestinal Tumor Markers Using a Machine Learning Algorithm and Multicolor Quantum Dot Biosensor.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- A review of applications of machine learning in quantum dots research.Discover nano · 2026Review
- Quantum dot enhanced photonic biosensors for single cell analysis from synthesis to clinical application.Discover nano · 2026Review
- Evaluation of the diagnostic value of combined gastrointestinal endoscopy and serum CEA and CA19-9 levels in gastrointestinal tumor.Medicine · 2025Article
- Quantum Oncology: The Applications of Quantum Computing in Cancer Research.Journal of medical systems · 2025Review
- Quantum Dot-Based Luminescent Sensors: Review from Analytical Perspective.International journal of molecular sciences · 2025Review
- Advancing Healthcare: Synergizing Biosensors and Machine Learning for Early Cancer Diagnosis.Biosensors · 2023Review
- Oncological Applications of Quantum Machine Learning.Technology in cancer research & treatmentReview
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3 authors.
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Abstract
This work was to explore the application value of gastrointestinal tumor markers based on gene feature selection model of principal component analysis (PCA) algorithm and multicolor quantum dots (QDs) immunobiosensor in the detection of gastrointestinal tumors. Based on the PCA method, the neighborhood rough set algorithm was introduced to improve it, and the tumor gene feature selection model (OPCA) was established to analyze its classification accuracy and accuracy. Four kinds of coupled biosensors were fabricated based on QDs, namely, 525 nm Cd Se/Zn S QDs-carbohydrate antigen 125 (QDs525-CA125 McAb), 605 nm Cd Se/Zn S QDs-cancer antigen 19-9 (QDs605-CA19-9 McAb), 645 nm Cd Se/Zn S QDs-anticancer embryonic antigen (QDs 645-CEA McAb), and 565 nm Cd Se/Zn S QDs-anti-alpha-fetoprotein (QDs565-AFP McAb). The quantum dot-antibody conjugates were identified and quantified by fluorescence spectroscopy and ultraviolet absorption spectroscopy. The results showed that the classification precision of OPCA model in colon tumor and gastric cancer datasets was 99.52% and 99.03%, respectively, and the classification accuracy was 94.86% and 94.2%, respectively, which were significantly higher than those of other algorithms. The fluorescence values of AFP McAb, CEA McAb, CA19-9 McAb, and CA125 McAb reached the maximum when the conjugation concentrations were 25
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