Annular RNA-disease association prediction method based on graph representation learning
The invention discloses a circular RNA-disease association prediction method based on graph representation learning, a mobile device and a storage medium, the method comprises: constructing a heterogeneous network of circular RNA based on circular RNA and related information, the heterogeneous network comprising circular RNA nodes and disease nodes; randomly initializing the features of each node in the heterogeneous network, inputting the features into a graph representation learning model, and learning the representation vector of each node according to a preset process through the graph representation learning model; and determining an association prediction score of the corresponding circular RNA and the disease based on an inner product of the representation vector of the circular RNA node and the representation vector of the disease node. Thus, the representation vector of each node in the heterogeneous network is learned through the graph representation learning model, and then the association prediction score is determined based on the inner product of the representation vectors of the circular RNA nodes and the disease nodes, so that the flexibility of heterogeneous network construction is improved, and the graph representation learning model can obtain richer node representation; and the accuracy of annular RNA-disease prediction is improved..
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Patent| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Europäisches Patentamt - (2024) vom: 12. Jan. Zur Gesamtaufnahme - year:2024 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
LI JUNYI [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| Themen: |
Sonstige Themen: |
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| Anmerkungen: |
Source: www.epo.org (no modifications made), First posted: 2024-01-12, Last update posted on www.tib.eu: 2024-04-30, Last updated: 2024-05-03 |
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| Patentnummer: |
CN117393143 |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
EPA001042734 |
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| 245 | 1 | 0 | |a Annular RNA-disease association prediction method based on graph representation learning |
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| 520 | |a The invention discloses a circular RNA-disease association prediction method based on graph representation learning, a mobile device and a storage medium, the method comprises: constructing a heterogeneous network of circular RNA based on circular RNA and related information, the heterogeneous network comprising circular RNA nodes and disease nodes; randomly initializing the features of each node in the heterogeneous network, inputting the features into a graph representation learning model, and learning the representation vector of each node according to a preset process through the graph representation learning model; and determining an association prediction score of the corresponding circular RNA and the disease based on an inner product of the representation vector of the circular RNA node and the representation vector of the disease node. Thus, the representation vector of each node in the heterogeneous network is learned through the graph representation learning model, and then the association prediction score is determined based on the inner product of the representation vectors of the circular RNA nodes and the disease nodes, so that the flexibility of heterogeneous network construction is improved, and the graph representation learning model can obtain richer node representation; and the accuracy of annular RNA-disease prediction is improved. | ||
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