Using Entropy as the Convergence Criteria of Ant Colony Optimization and the Application at Gene Chip Data Analysis

Chonghao Gao; Xinping Pang; Chongbao Wang; Jingyue Huang; Hui Liu; Chengjiang Zhu; Kunpei Jin; Weiqi Li; Pengtao Zheng; Zihang Zeng; Yanyu Wei; Chaoyang Pang

doi:10.2174/0115672050325388240823092338

Using Entropy as the Convergence Criteria of Ant Colony Optimization and the Application at Gene Chip Data Analysis

Авторы: Gao C.¹, Pang X.², Wang C.³, Huang J.¹, Liu H.⁴, Zhu C.¹, Jin K.¹, Li W.¹, Zheng P.¹, Zeng Z.¹, Wei Y.⁵, Pang C.¹
Учреждения:
1. College of Computer Science, Sichuan Normal University
2. West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University
3. , Beijing Magicnurse Surgical Robot Technology Co. Ltd.
4. School of Information and Software Engineering, University of Electronic Science and Technology of China
5. National Key Laboratory of Science and Technology on Vacuum Electronics, School of Electronic Science and Engineering, University of Electronic Science and Technology of China
Выпуск: Том 21, № 5 (2024)
Страницы: 324-341
Раздел: Medicine
URL: https://kazanmedjournal.ru/1567-2050/article/view/643809
DOI: https://doi.org/10.2174/0115672050325388240823092338
ID: 643809

Цитировать

Полный текст

Аннотация

Introduction:When Ant Colony Optimization algorithm (ACO) is adept at identifying the shortest path, the temporary solution is uncertain during the iterative process. All temporary solutions form a solution set.

Methods:Where each solution is random. That is, the solution set has entropy. When the solution tends to be stable, the entropy also converges to a fixed value. Therefore, it was proposed in this paper that apply entropy as a convergence criterion of ACO. The advantage of the proposed criterion is that it approximates the optimal convergence time of the algorithm.

Results:In order to prove the superiority of the entropy convergence criterion, it was used to cluster gene chip data, which were sampled from patients of Alzheimers Disease (AD). The clustering algorithm is compared with six typical clustering algorithms. The comparison shows that the ACO using entropy as a convergence criterion is of good quality.

Conclusion:At the same time, applying the presented algorithm, we analyzed the clustering characteristics of genes related to energy metabolism and found that as AD occurs, the entropy of the energy metabolism system decreases; that is, the system disorder decreases significantly.

Ключевые слова

Ant colony algorithms, entropy, Alzheimer&, rsquo, s disease, gene chip data analysis, entropy convergence criterion, energy metabolism system.

Дополнительные файлы

Доп. файлы

Действие

1. JATS XML

Скачать

Имя пользователя
Пароль
Запомнить меня

Забыли пароль?	Регистрация

Имя пользователя
Пароль
Запомнить меня

Забыли пароль?	Регистрация

Using Entropy as the Convergence Criteria of Ant Colony Optimization and the Application at Gene Chip Data Analysis

Полный текст

Аннотация

Ключевые слова

Об авторах

Дополнительные файлы