Background: The explosive growth of genomic, chemical, and pathological data provides new opportunities and challenges for humans to thoroughly understand life activities in cells. However, there exist few computational models that aggregate various bioentities to comprehensively reveal the physical and functional landscape of biological systems.
Results: We constructed a molecular association network, which contains 18 edges (relationships) between 8 nodes (bioentities).
The edges and nodes can be found in the above files.
If you find the data is useful for your research, please consider citing the following paper:
[3] Guo, Z. H., You, Z. H., Wang, Y. B., Huang, D. S., Yi, H. C., & Chen, Z. H. (2020). Bioentity2vec: Attribute-and
behavior-driven representation for predicting multi-type relationships between bioentities. GigaScience, 9(6), giaa032.
[2] Guo, Z. H., You, Z. H., Huang, D. S., Yi, H. C., Chen, Z. H., & Wang, Y. B. (2020). A learning based framework for
diverse biomolecule relationship prediction in molecular association network. Communications biology, 3(1), 118.
[1] Guo, Z. H., You, Z. H., & Yi, H. C. (2020). Integrative construction and analysis of molecular association network
in human cells by fusing node attribute and behavior information. Molecular Therapy-Nucleic Acids, 19, 498-506.