Glioma prognosis model construction method and device based on n7-methylguanosine related long-chain non-coding RNA
The invention relates to a glioma prognosis model construction method and device based on n7-methylguanosine related long-chain non-coding RNA (Ribonucleic Acid). The method comprises the following steps: acquiring glioma patient data and transcriptome data related to n7-methylguanosine; constructing a test data set and a verification data set based on glioma patient data and transcriptome data; based on an LASSO regression analysis method and a random forest algorithm, screening out a plurality of n7-methylguanosine-related long-chain non-coding RNAs (Ribonucleic Acid) for the prognosis of the glioma patient from the test data set; establishing a glioma prognosis model of the n7-methylguanosine related long-chain non-coding RNA (Ribonucleic Acid); and performing statistical analysis, gene enrichment analysis and immune pathway analysis on the prediction result of the glioma prognosis model by verifying the data set. Through combination of gene analysis, machine learning and statistical methods, the specificity and accuracy of the glioma prognosis model are improved..
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Patent| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Europäisches Patentamt - (2023) vom: 31. Okt. Zur Gesamtaufnahme - year:2023 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
LIU YUE [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: 2023-10-31, Last update posted on www.tib.eu: 2024-01-22, Last updated: 2024-01-26 |
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| Patentnummer: |
CN116978557 |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
EPA018998909 |
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| 520 | |a The invention relates to a glioma prognosis model construction method and device based on n7-methylguanosine related long-chain non-coding RNA (Ribonucleic Acid). The method comprises the following steps: acquiring glioma patient data and transcriptome data related to n7-methylguanosine; constructing a test data set and a verification data set based on glioma patient data and transcriptome data; based on an LASSO regression analysis method and a random forest algorithm, screening out a plurality of n7-methylguanosine-related long-chain non-coding RNAs (Ribonucleic Acid) for the prognosis of the glioma patient from the test data set; establishing a glioma prognosis model of the n7-methylguanosine related long-chain non-coding RNA (Ribonucleic Acid); and performing statistical analysis, gene enrichment analysis and immune pathway analysis on the prediction result of the glioma prognosis model by verifying the data set. Through combination of gene analysis, machine learning and statistical methods, the specificity and accuracy of the glioma prognosis model are improved. | ||
| 650 | 4 | |a G16B: Bioinformatics, i.e. information and communication technology [ict] specially adapted for genetic or protein-related data processing in computational molecular biology | |
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