@inproceedings{ec2717c6987f404891f80b093c8fa9c5,
title = "Classification of Tumor Metastasis Data by Using Quantum kernel-based Algorithms",
abstract = "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.",
keywords = "biomarkers, precision medicine, quantum kernel-based classifier, quantum machine learning, tumor metastasis",
author = "Li, {Tai Yue} and Mekala, {Venugopala Reddy} and Ng, {Ka Lok} and Su, {Cheng Fang}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 22nd IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2022 ; Conference date: 07-11-2022 Through 09-11-2022",
year = "2022",
doi = "10.1109/BIBE55377.2022.00078",
language = "English",
series = "Proceedings - IEEE 22nd International Conference on Bioinformatics and Bioengineering, BIBE 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "351--354",
booktitle = "Proceedings - IEEE 22nd International Conference on Bioinformatics and Bioengineering, BIBE 2022",
address = "United States",
}