Institution
Xuzhou Institute of Technology
Education•Xuzhou, China•
About: Xuzhou Institute of Technology is a education organization based out in Xuzhou, China. It is known for research contribution in the topics: Catalysis & Adsorption. The organization has 1696 authors who have published 1521 publications receiving 13541 citations.
Papers published on a yearly basis
Papers
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TL;DR: An improved path planning for mobile robots is proposed based on the hybrid multi-objective bare-bones particle swarm optimization with differential evolution, and a new Pareto domination with collision constraints is developed to select the personal best position of a particle according to the definition of the collision degree of a path.
Abstract: An improved path planning for mobile robots is proposed based on the hybrid multi-objective bare-bones particle swarm optimization with differential evolution. The mathematical model for robot path planning is firstly devised as a tri-objective optimization with three indices, i.e., the path length, the smoothness degree of a path, and the safety degree of a path. Then, a hybrid multi-objective bare bones particle swarm optimization is developed to generate feasible paths by combining infeasible paths blocked by obstacles with feasible paths via improved mutation strategies of differential evolution. In addition, a new Pareto domination with collision constraints is developed to select the personal best position of a particle according to the definition of the collision degree of a path. Simulation results confirm the effectiveness of our algorithm.
56 citations
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56 citations
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TL;DR: In this paper, a class of CoSe2 nano-vesicle that derived from ZIF-67 nanocubes is fabricated by a facile one-pot hydrothermal method.
55 citations
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TL;DR: A novel graph auto-encoder model, named GAEMDA, is proposed, which applies a graph neural networks-based encoder, which contains aggregator function and multi-layer perceptron for aggregating nodes' neighborhood information, to generate the low-dimensional embeddings of miRNA and disease nodes and realize the effective fusion of heterogeneous information.
Abstract: Emerging evidence indicates that the abnormal expression of miRNAs involves in the evolution and progression of various human complex diseases. Identifying disease-related miRNAs as new biomarkers can promote the development of disease pathology and clinical medicine. However, designing biological experiments to validate disease-related miRNAs is usually time-consuming and expensive. Therefore, it is urgent to design effective computational methods for predicting potential miRNA-disease associations. Inspired by the great progress of graph neural networks in link prediction, we propose a novel graph auto-encoder model, named GAEMDA, to identify the potential miRNA-disease associations in an end-to-end manner. More specifically, the GAEMDA model applies a graph neural networks-based encoder, which contains aggregator function and multi-layer perceptron for aggregating nodes' neighborhood information, to generate the low-dimensional embeddings of miRNA and disease nodes and realize the effective fusion of heterogeneous information. Then, the embeddings of miRNA and disease nodes are fed into a bilinear decoder to identify the potential links between miRNA and disease nodes. The experimental results indicate that GAEMDA achieves the average area under the curve of $93.56\pm 0.44\%$ under 5-fold cross-validation. Besides, we further carried out case studies on colon neoplasms, esophageal neoplasms and kidney neoplasms. As a result, 48 of the top 50 predicted miRNAs associated with these diseases are confirmed by the database of differentially expressed miRNAs in human cancers and microRNA deregulation in human disease database, respectively. The satisfactory prediction performance suggests that GAEMDA model could serve as a reliable tool to guide the following researches on the regulatory role of miRNAs. Besides, the source codes are available at https://github.com/chimianbuhetang/GAEMDA.
55 citations
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TL;DR: Garlic can reduce the level of TC and LDL instead of HDL and TG, indicating the ability of anti-hyperlipidemia.
55 citations
Authors
Showing all 1711 results
Name | H-index | Papers | Citations |
---|---|---|---|
Peng Wang | 108 | 1672 | 54529 |
Qiong Wu | 51 | 316 | 12933 |
Wenping Cao | 34 | 176 | 4093 |
Bin Hu | 30 | 213 | 3121 |
Syed Abdul Rehman Khan | 29 | 131 | 2733 |
Jingui Duan | 29 | 93 | 3807 |
Vivian C.H. Wu | 25 | 105 | 2566 |
Lei Chen | 16 | 99 | 1062 |
Chao Wang | 16 | 74 | 741 |
Wenbin Gong | 16 | 27 | 953 |
Jing Li | 16 | 40 | 1025 |
Chao Liu | 15 | 43 | 737 |
Qinglin Wang | 14 | 72 | 595 |
Yaocheng Zhang | 14 | 54 | 566 |
Chao Wang | 13 | 25 | 774 |