TY - GEN
T1 - Classification of Tumor Metastasis Data by Using Quantum kernel-based Algorithms
AU - Li, Tai Yue
AU - Mekala, Venugopala Reddy
AU - Ng, Ka Lok
AU - Su, Cheng Fang
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Tumor metastasis is a dynamic process, and its fatality rate is relatively high. Different genes are regulated during the metastasis process. Next-generation sequencing technology is a new approach that enables rapid and high throughput whole-transcriptome measurements. Support vectoTalgorithms make use of kernel functions to classify data effectively. A few studies suggest that quantum support vector machine algorithms can perform well in classification problems. If biomarkers can be identified to predict tumor metastasis accurately, it will be an important step toward precision medicine. In this study, we use both the SVM and QSVM classifiers with the addition of a certain number of features, we can achieve very good distinctions between patients with or without metastasis. This is a positive result for precision medicine studies. Also, we evaluate the performance of quantum and classical algorithms in classifying tumor metastasis data. Our preliminary study indicates that the classical kernel-based classifier performs better than the quantum version.
AB - Tumor metastasis is a dynamic process, and its fatality rate is relatively high. Different genes are regulated during the metastasis process. Next-generation sequencing technology is a new approach that enables rapid and high throughput whole-transcriptome measurements. Support vectoTalgorithms make use of kernel functions to classify data effectively. A few studies suggest that quantum support vector machine algorithms can perform well in classification problems. If biomarkers can be identified to predict tumor metastasis accurately, it will be an important step toward precision medicine. In this study, we use both the SVM and QSVM classifiers with the addition of a certain number of features, we can achieve very good distinctions between patients with or without metastasis. This is a positive result for precision medicine studies. Also, we evaluate the performance of quantum and classical algorithms in classifying tumor metastasis data. Our preliminary study indicates that the classical kernel-based classifier performs better than the quantum version.
KW - biomarkers
KW - precision medicine
KW - quantum kernel-based classifier
KW - quantum machine learning
KW - tumor metastasis
UR - http://www.scopus.com/inward/record.url?scp=85145608268&partnerID=8YFLogxK
U2 - 10.1109/BIBE55377.2022.00078
DO - 10.1109/BIBE55377.2022.00078
M3 - Conference contribution
AN - SCOPUS:85145608268
T3 - Proceedings - IEEE 22nd International Conference on Bioinformatics and Bioengineering, BIBE 2022
SP - 351
EP - 354
BT - Proceedings - IEEE 22nd International Conference on Bioinformatics and Bioengineering, BIBE 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 22nd IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2022
Y2 - 7 November 2022 through 9 November 2022
ER -