Classification of Tumor Metastasis Data by Using Quantum kernel-based Algorithms

Tai Yue Li, Venugopala Reddy Mekala, Ka Lok Ng*, Cheng Fang Su*

*此作品的通信作者

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題Proceedings - IEEE 22nd International Conference on Bioinformatics and Bioengineering, BIBE 2022
發行者Institute of Electrical and Electronics Engineers Inc.
頁面351-354
頁數4
ISBN(電子)9781665484879
DOIs
出版狀態Published - 2022
事件22nd IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2022 - Virtual, Online, Taiwan
持續時間: 7 11月 20229 11月 2022

出版系列

名字Proceedings - IEEE 22nd International Conference on Bioinformatics and Bioengineering, BIBE 2022

Conference

Conference22nd IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2022
國家/地區Taiwan
城市Virtual, Online
期間7/11/229/11/22

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