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使用分支定界算法对Ⅱ型糖尿病爪哇配方进行二分图搜索优化

Bipartite graph search optimization for type II diabetes mellitus Jamu formulation using branch and bound algorithm.

作者信息

Kusuma Wisnu Ananta, Habibi Zulfahmi Ibnu, Amir Muhammad Fahmi, Fadli Aulia, Khotimah Husnul, Dewanto Vektor, Heryanto Rudi

机构信息

Department of Computer Science, Faculty of Mathematics and Natural Sciences, IPB University, Bogor, Indonesia.

Tropical Biopharmaca Research Center, IPB University, Bogor, Indonesia.

出版信息

Front Pharmacol. 2022 Aug 11;13:978741. doi: 10.3389/fphar.2022.978741. eCollection 2022.

Abstract

Jamu is an Indonesian traditional herbal medicine that has been practiced for generations. Jamu is made from various medicinal plants. Each plant has several compounds directly related to the target protein that are directly associated with a disease. A pharmacological graph can form relationships between plants, compounds, and target proteins. Research related to the prediction of Jamu formulas for some diseases has been carried out, but there are problems in finding combinations or compositions of Jamu formulas because of the increase in search space size. Some studies adopted the drug-target interaction (DTI) implemented using machine learning or deep learning to predict the DTI for discovering the Jamu formula. However, this approach raises important issues, such as imbalanced and high-dimensional dataset, overfitting, and the need for more procedures to trace compounds to their plants. This study proposes an alternative approach by implementing bipartite graph search optimization using the branch and bound algorithm to discover the combination or composition of Jamu formulas by optimizing the search on a plant-protein bipartite graph. The branch and bound technique is implemented using the search strategy of breadth first search (BrFS), Depth First Search, and Best First Search. To show the performance of the proposed method, we compared our method with a complete search algorithm, searching all nodes in the tree without pruning. In this study, we specialize in applying the proposed method to search for the Jamu formula for type II diabetes mellitus (T2DM). The result shows that the bipartite graph search with the branch and bound algorithm reduces computation time up to 40 times faster than the complete search strategy to search for a composition of plants. The binary branching strategy is the best choice, whereas the BrFS strategy is the best option in this research. In addition, the the proposed method can suggest the composition of one to four plants for the T2DM Jamu formula. For a combination of four plants, we obtain , , , and . This approach is expected to be an alternative way to discover the Jamu formula more accurately.

摘要

爪哇草药是一种印度尼西亚传统草药,已传承了几代人。爪哇草药由多种药用植物制成。每种植物都含有几种与目标蛋白直接相关的化合物,这些化合物与某种疾病直接相关。药理学图谱可以在植物、化合物和目标蛋白之间形成关系。已经开展了一些关于预测某些疾病的爪哇草药配方的研究,但由于搜索空间大小的增加,在寻找爪哇草药配方的组合或成分时存在问题。一些研究采用了使用机器学习或深度学习实现的药物 - 靶点相互作用(DTI)来预测DTI,以发现爪哇草药配方。然而,这种方法引发了一些重要问题,例如数据集不平衡和高维、过拟合,以及需要更多程序来将化合物追溯到其植物来源。本研究提出了一种替代方法,通过使用分支定界算法实现二分图搜索优化,在植物 - 蛋白质二分图上进行优化搜索,以发现爪哇草药配方的组合或成分。分支定界技术通过广度优先搜索(BrFS)、深度优先搜索和最佳优先搜索的搜索策略来实现。为了展示所提出方法的性能,我们将我们的方法与完全搜索算法进行了比较,完全搜索算法会搜索树中的所有节点而不进行剪枝。在本研究中,我们专门将所提出的方法应用于搜索II型糖尿病(T2DM)的爪哇草药配方。结果表明,使用分支定界算法的二分图搜索在搜索植物成分时,计算时间比完全搜索策略快40倍。二叉分支策略是最佳选择,而BrFS策略是本研究中的最佳选项。此外,所提出的方法可以为T2DM爪哇草药配方建议一到四种植物的成分。对于四种植物的组合,我们得到了[此处原文缺失具体数据]。这种方法有望成为更准确地发现爪哇草药配方的一种替代方式。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ece/9403330/d53a6b13744c/fphar-13-978741-g001.jpg

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