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.
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http://dx.doi.org/10.3389/fphar.2022.978741 | DOI Listing |
Sci Data
December 2024
Biology Centre of the Czech Academy of Sciences, Institute of Entomology, Department of Ecology, Ceske Budejovice, Czech Republic.
Pathogens significantly influence natural and agricultural ecosystems, playing a crucial role in the regulation of species populations and maintaining biodiversity. Entomopathogenic fungi (EF), particularly within the Hypocreales order, exemplify understudied pathogens that infect insects and other arthropods globally. Despite their ecological importance, comprehensive data on EF host specificity and geographical distribution are lacking.
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December 2024
University of Strasbourg, CAMMA, ICube, CNRS, INSERM, France; IHU Strasbourg, Strasbourg, France.
Accurate tool tracking is essential for the success of computer-assisted intervention. Previous efforts often modeled tool trajectories rigidly, overlooking the dynamic nature of surgical procedures, especially tracking scenarios like out-of-body and out-of-camera views. Addressing this limitation, the new CholecTrack20 dataset provides detailed labels that account for multiple tool trajectories in three perspectives: (1) intraoperative, (2) intracorporeal, and (3) visibility, representing the different types of temporal duration of tool tracks.
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October 2024
Changchun Children's Library, Changchun, Jilin, China.
Heliyon
November 2024
Department of Computer and Information Sciences, Northumbria University, Newcastle NE1 8ST, UK.
Let be a simple connected graph of order having Wiener index . The distance, distance Laplacian and the distance signless Laplacian energies of are respectively defined as where and are respectively the distance, distance Laplacian and the distance signless Laplacian eigenvalues of and is the average transmission degree. In this paper, we will study the relation between , and .
View Article and Find Full Text PDFNat Commun
November 2024
James Franck Institute, The University of Chicago, Chicago, IL, USA.
The physics of complex systems stands to greatly benefit from the qualitative changes in data availability and advances in data-driven computational methods. Many of these systems can be represented by interacting degrees of freedom on inhomogeneous graphs. However, the lack of translational invariance presents a fundamental challenge to theoretical tools, such as the renormalization group, which were so successful in characterizing the universal physical behaviour in critical phenomena.
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