Quantitative Detection of Gastrointestinal Tumor Markers Using a Machine Learning Algorithm and Multicolor Quantum Dot Biosensor

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 µg/mL, 20 µg/mL, 30 µg/mL, and 30 µg/m, respectively. The highest recovery rate of AFP was 98.51%, and its fluorescence intensity was 35.78 ± 2.99, which was significantly higher than that of other antigens ( P < 0.001 ). In summary, the OPCA model based on PCA algorithm can obtain fewer feature gene sets and improve the accuracy of sample classification. Intelligent immunobiosensors based on machine learning algorithms and QDs have potential application value in gastrointestinal gene feature selection and tumor marker detection, which provides a new idea for clinical diagnosis of gastrointestinal tumors.

Oncological Applications of Quantum Machine Learning

“…Another article 26 employed the quantum genetic algorithm to solve the multilevel threshold problem. In 1 article, 31 the quantum-inspired immune clone optimization (QICO) algorithm based on the quantum computing theory was used to select the optimal feature for classifying cancer data. Furthermore, the quantum physics-based quantum measurement regression (QMR) algorithm 30 was used to improve the interpretability of results, and the entered articles were analyzed separately ( Table 3 ).…”

“…Article 31 demonstrates that the QICO-based feature selection model outperforms other discovery-based models, and the optimized RNN achieves better results than other ML methods.…”

Advancing Healthcare: Synergizing Biosensors and Machine Learning for Early Cancer Diagnosis

“…Quantum dot immunobionsensors are powerful optical sensors used to detect cancer cells, which were introduced by Saren et al [ 82 ] to detect and quantify gastrointestinal tumor biomarkers. They developed quantum dot (QD)-labeled biofilms to detect four biomarkers: CEA, CA125, CA19-9, and AFP, indicating the presence of gastrointestinal tumors.…”