| Citation: | ZHU Xuesong, QU Enrui, ZHU Yufeng, QUAN Yuan. Construction and Application of a Ternary Relationship Prediction Model for Microgravity Biological Knowledge Graph (in Chinese). Chinese Journal of Space Science, 2025, 45(2): 493-505 doi: 10.11728/cjss2025.02.2024-0167
|
| [1] |
KIM D H, LIN S C. Human capital and natural resource dependence[J]. Structural Change and Economic Dynamics, 2017, 40: 92-102 doi: 10.1016/j.strueco.2017.01.002
|
| [2] |
DREES J M, HEUGENS P P M A R. Synthesizing and extending resource dependence theory: a meta-analysis[J]. Journal of Management, 2013, 39(6): 1666-1698 doi: 10.1177/0149206312471391
|
| [3] |
FANKHAUSER T, WANG Q, GERLICHER A, et al. Resource dependency processing in web scaling frameworks[J]. IEEE Transactions on Services Computing, 2018, 11(1): 155-168 doi: 10.1109/TSC.2016.2561934
|
| [4] |
DICKERSON B L, SOWINSKI R, KREIDER R B, et al. Impacts of microgravity on amino acid metabolism during spaceflight[J]. Experimental Biology and Medicine, 2023, 248(5): 380-393 doi: 10.1177/15353702221139189
|
| [5] |
OLUWAFEMI F A, ABDELBAKI R, LAI J C Y, et al. A review of astronaut mental health in manned missions: Potential interventions for cognitive and mental health challenges[J]. Life Sciences in Space Research, 2021, 28: 26-31 doi: 10.1016/j.lssr.2020.12.002
|
| [6] |
SPRUGNOLI G, CAGLE Y D, SANTARNECCHI E. Microgravity and cosmic radiations during space exploration as a window into neurodegeneration on earth[J]. JAMA Neurology, 2020, 77(2): 157-158 doi: 10.1001/jamaneurol.2019.4003
|
| [7] |
VAN OMBERGEN A, DEMERTZI A, TOMILOVSKAYA E, et al. The effect of spaceflight and microgravity on the human brain[J]. Journal of Neurology, 2017, 264(1): 18-22
|
| [8] |
ZHENG D, SONG X, MA C, et al. DGL-KE: training knowledge graph embeddings at scale[C]//Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2020
|
| [9] |
SANG S T, YANG Z H, LIU X X, et al. GrEDeL: a knowledge graph embedding based method for drug discovery from biomedical literatures[J]. IEEE Access, 2018, 7: 8404-8415
|
| [10] |
FARRUGIA L, AZZOPARDI L M, DEBATTISTA J, et al. Predicting drug-drug interactions using knowledge graphs[OL]. arXiv preprint arXiv: 2308.04172, 2023
|
| [11] |
ZHENG Y H, PAN G J, QUAN Y, et al. Construction of microgravity biological knowledge graph and its applications in anti-osteoporosis drug prediction, Life Sciences in Space Research, Volume 41, 2024, Pages 64-73, ISSN 2214-5524.
|
| [12] |
Ioannidis, V. N. et al. Drkg-drug repurposing knowledge graph for covid-19. GitHub https://github.com/gnn4dr /DRKG (2020). Accessed 01 Jan 2022.
|
| [13] |
ZHU G H, ZHU Z N, CHEN H Y, et al. HAGNN: hybrid aggregation for heterogeneous graph neural networks[OL]. arXiv preprint arXiv: 2307.01636, 2023
|
| [14] |
LIANG T, LIU J. Meta-path generation online for heterogeneous network embedding[C]//Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN). Glasgow, UK: IEEE, 2020
|
| [15] |
RODRIGUEZ J D, PEREZ A, LOZANO J A. Sensitivity analysis of k-fold cross validation in prediction error estimation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(3): 569-575 doi: 10.1109/TPAMI.2009.187
|
| [16] |
POHJANKUKKA J, PAHIKKALA T, NEVALAINEN P, et al. Estimating the prediction performance of spatial models via spatial k-fold cross validation[J]. International Journal of Geographical Information Science, 2017, 31(10): 2001-2019 doi: 10.1080/13658816.2017.1346255
|
| [17] |
BERGMEIR C, HYNDMAN R J, KOO B. A note on the validity of cross-validation for evaluating autoregressive time series prediction[J]. Computational Statistics :Times New Roman;">& Data Analysis, 2018, 120: 70-83
|
| [18] |
SAHA S, ROY D, MITRA M. On modifying evaluation measures to deal with ties in ranked lists[C]//Proceedings of the 22nd ACM/IEEE Joint Conference on Digital Libraries. Cologne, Germany: IEEE, 2022
|
| [19] |
GOEL S, KUMAR R, KUMAR M, et al. An efficient page ranking approach based on vector norms using sNorm(p) algorithm[J]. Information Processing :Times New Roman;">& Management, 2019, 56(3): 1053-1066
|
| [20] |
SADEGHI A, GRAUX D, YAZDI H S, et al. MDE: multiple distance embeddings for link prediction in knowledge graphs[OL]. arXiv preprint arXiv: 1905.10702, 2019
|
| [21] |
TURAL S, KARA N, ALAYLI G, et al. Association between osteoporosis and polymorphisms of the bone Gla protein, estrogen receptor 1, collagen 1-A1 and calcitonin receptor genes in Turkish postmenopausal women[J]. Gene, 2013, 515(1): 167-172 doi: 10.1016/j.gene.2012.10.041
|
| [22] |
SEHMISCH S, UFFENORDE J, MAEHLMEYER S, et al. Evaluation of bone quality and quantity in osteoporotic mice–the effects of genistein and equol[J]. Phytomedicine, 2010, 17(6): 424-430 doi: 10.1016/j.phymed.2009.10.004
|
| [23] |
Da Silva F R P, Vasconcelos A C C G, Casimiro G S, et al. Quantitative assessment of the association between polymorphisms in osteoprotegerin gene and risk of low bone mineral density[J]. International Archives of Medicine, 2015: 8(169)
|
| [24] |
ŞIRIN Ö K, AYDOĞAN H Y, UYAR M, et al. PPARγ Pro12Ala and C161T polymorphisms, but not PPARα L162V, are associated with osteoporosis risk in Turkish postmenopausal women[J]. İstanbul Journal of Pharmacy, 2019, 49(1): 14-19
|
| [25] |
SCHOLTYSEK C, KATZENBEISSER J, FU H, et al. PPARβ/δ governs Wnt signaling and bone turnover[J]. Nature Medicine, 2013, 19(5): 608-613 doi: 10.1038/nm.3146
|
| [26] |
ZAYZAFOON M, GATHINGS W E, MCDONALD J M. Modeled microgravity inhibits osteogenic differentiation of human mesenchymal stem cells and increases Adipogenesis[J]. Endocrinology, 2004, 145(5): 2421-2432 doi: 10.1210/en.2003-1156
|
| [27] |
MANN V, GRIMM D, CORYDON T J, et al. Changes in human foetal osteoblasts exposed to the random positioning machine and bone construct tissue engineering[J]. International Journal of Molecular Sciences, 2019, 20(6): 1357 doi: 10.3390/ijms20061357
|
| [28] |
JURZAK M, ADAMCZYK K, ANTOŃCZAK P, et al. Evaluation of genistein ability to modulate CTGF mRNA/protein expression, genes expression of TGFβ isoforms and expression of selected genes regulating cell cycle in keloid fibroblasts in vitro[J]. Acta Poloniae Pharmaceutica, 2014, 71(6): 972-986
|
| [29] |
Plourde P V, SCHWARTZBERG L S, GREENE G L, et al. An open-label, randomized, multi-center phase 2 study evaluating the activity of lasofoxifene relative to fulvestrant for the treatment of postmenopausal women with locally advanced or Metastatic ER+/HER2- Breast Cancer (MBC) with an ESR1 mutation[C]//Cancer Research, San Antonio: San Antonio Breast Cancer Symposium, 2019
|
| [30] |
GOETZ M P, GAL-YAM E, STOVER D, et al. Abstract P5-05-04: Estrogen Receptor 1 (ESR1) mutations in circulating tumor DNA (ctDNA) from patients with ER+/HER2-metastatic Breast Cancer (mBC) treated with lasofoxifene or fulvestrant in the ELAINE 1 study[J]. Cancer Research, 2023, 83(S5): P5-05-04
|
| [31] |
SEFRIOUI D, PERDRIX A, SARAFAN-VASSEUR N, et al. Short report: monitoring ESR1 mutations by circulating tumor DNA in aromatase inhibitor resistant metastatic breast cancer[J]. International Journal of Cancer, 2015, 137(10): 2513-2519 doi: 10.1002/ijc.29612
|
| [32] |
NOORDIN S, GLOWACKI J. Parathyroid hormone and its receptor gene polymorphisms: implications in osteoporosis and in fracture healing[J]. Rheumatology International, 2016, 36(1): 1-6 doi: 10.1007/s00296-015-3319-9
|
| [33] |
STYRKARSDOTTIR U, THORLEIFSSON G, GUDJONSSON S A, et al. Sequence variants in the PTCH1 gene associate with spine bone mineral density and osteoporotic fractures[J]. Nature Communications, 2016, 7(1): 10129 doi: 10.1038/ncomms10129
|
| [34] |
NAKAMURA T, SUGIMOTO T, NAKANO T, et al. Randomized Teriparatide [Human Parathyroid Hormone (PTH) 1–34] Once-Weekly Efficacy Research (TOWER) trial for examining the reduction in new vertebral fractures in subjects with primary osteoporosis and high fracture risk[J]. The Journal of Clinical Endocrinology :Times New Roman;">& Metabolism, 2012, 97(9): 3097-3106
|
| [35] |
CUMMINGS S R, MCCLUNG M, REGINSTER J Y, et al. Arzoxifene for prevention of fractures and invasive breast cancer in postmenopausal women[J]. Journal of Bone and Mineral Research, 2011, 26(2): 397-404 doi: 10.1002/jbmr.191
|
| [36] |
VICO L, HARGENS A. Skeletal changes during and after spaceflight[J]. Nature Reviews Rheumatology, 2018, 14(2): 229-245
|
| [37] |
SMITH S M, HEER M A, SHACKELFORD L C, et al. Benefits for bone from resistance exercise and nutrition in long-duration spaceflight[J]. Journal of Applied Physiology, 2012, 112(1): 105-113
|
| [38] |
CARTER J A, BUCKEY J C, GREENHALGH L, et al. An interactive media program for managing psychosocial problems on long-duration spaceflights[J]. Aviation, Space, and Environmental Medicine, 2005, 76(S6): B213-B223
|
Figures(4) / Tables(5)
Journal Introduction
ISSN 0254-6124 CN 11-1783/V
Copyright © Editorial Office of Chinese Journal of Space Science. 京ICP备08100722号
Supported by: Beijing Renhe Information Technology Co., Ltd.
DownLoad:
