Bioinformatics approach to identify the hub gene associated with COVID‐19 and idiopathic pulmonary fibrosis
Abstract The coronavirus disease 2019 (COVID‐19) has developed into a global health crisis. Pulmonary fibrosis, as one of the complications of SARS‐CoV‐2 infection, deserves attention. As COVID‐19 is a new clinical entity that is constantly evolving, and many aspects of disease are remain unknown. T...
Ausführliche Beschreibung
Autor*in: |
Wenchao Shi [verfasserIn] Tinghui Li [verfasserIn] Huiwen Li [verfasserIn] Juan Ren [verfasserIn] Meiyu Lv [verfasserIn] Qi Wang [verfasserIn] Yaowu He [verfasserIn] Yao Yu [verfasserIn] Lijie Liu [verfasserIn] Shoude Jin [verfasserIn] Hong Chen [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2023 |
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Übergeordnetes Werk: |
In: IET Systems Biology - Wiley, 2021, 17(2023), 6, Seite 336-351 |
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Übergeordnetes Werk: |
volume:17 ; year:2023 ; number:6 ; pages:336-351 |
Links: |
Link aufrufen |
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DOI / URN: |
10.1049/syb2.12080 |
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Katalog-ID: |
DOAJ099131161 |
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520 | |a Abstract The coronavirus disease 2019 (COVID‐19) has developed into a global health crisis. Pulmonary fibrosis, as one of the complications of SARS‐CoV‐2 infection, deserves attention. As COVID‐19 is a new clinical entity that is constantly evolving, and many aspects of disease are remain unknown. The datasets of COVID‐19 and idiopathic pulmonary fibrosis were obtained from the Gene Expression Omnibus. The hub genes were screened out using the Random Forest (RF) algorithm depending on the severity of patients with COVID‐19. A risk prediction model was developed to assess the prognosis of patients infected with SARS‐CoV‐2, which was evaluated by another dataset. Six genes (named NELL2, GPR183, S100A8, ALPL, CD177, and IL1R2) may be associated with the development of PF in patients with severe SARS‐CoV‐2 infection. S100A8 is thought to be an important target gene that is closely associated with COVID‐19 and pulmonary fibrosis. Construction of a neural network model was successfully predicted the prognosis of patients with COVID‐19. With the increasing availability of COVID‐19 datasets, bioinformatic methods can provide possible predictive targets for the diagnosis, treatment, and prognosis of the disease and show intervention directions for the development of clinical drugs and vaccines. | ||
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10.1049/syb2.12080 doi (DE-627)DOAJ099131161 (DE-599)DOAJ01a2032629184f729c91c6b4fcf9d0ca DE-627 ger DE-627 rakwb eng QH301-705.5 Wenchao Shi verfasserin aut Bioinformatics approach to identify the hub gene associated with COVID‐19 and idiopathic pulmonary fibrosis 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The coronavirus disease 2019 (COVID‐19) has developed into a global health crisis. Pulmonary fibrosis, as one of the complications of SARS‐CoV‐2 infection, deserves attention. As COVID‐19 is a new clinical entity that is constantly evolving, and many aspects of disease are remain unknown. The datasets of COVID‐19 and idiopathic pulmonary fibrosis were obtained from the Gene Expression Omnibus. The hub genes were screened out using the Random Forest (RF) algorithm depending on the severity of patients with COVID‐19. A risk prediction model was developed to assess the prognosis of patients infected with SARS‐CoV‐2, which was evaluated by another dataset. Six genes (named NELL2, GPR183, S100A8, ALPL, CD177, and IL1R2) may be associated with the development of PF in patients with severe SARS‐CoV‐2 infection. S100A8 is thought to be an important target gene that is closely associated with COVID‐19 and pulmonary fibrosis. Construction of a neural network model was successfully predicted the prognosis of patients with COVID‐19. With the increasing availability of COVID‐19 datasets, bioinformatic methods can provide possible predictive targets for the diagnosis, treatment, and prognosis of the disease and show intervention directions for the development of clinical drugs and vaccines. big data bioinformatics Biology (General) Tinghui Li verfasserin aut Huiwen Li verfasserin aut Juan Ren verfasserin aut Meiyu Lv verfasserin aut Qi Wang verfasserin aut Yaowu He verfasserin aut Yao Yu verfasserin aut Lijie Liu verfasserin aut Shoude Jin verfasserin aut Hong Chen verfasserin aut In IET Systems Biology Wiley, 2021 17(2023), 6, Seite 336-351 (DE-627)521693756 (DE-600)2264538-X 17518857 nnns volume:17 year:2023 number:6 pages:336-351 https://doi.org/10.1049/syb2.12080 kostenfrei https://doaj.org/article/01a2032629184f729c91c6b4fcf9d0ca kostenfrei https://doi.org/10.1049/syb2.12080 kostenfrei https://doaj.org/toc/1751-8849 Journal toc kostenfrei https://doaj.org/toc/1751-8857 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 17 2023 6 336-351 |
spelling |
10.1049/syb2.12080 doi (DE-627)DOAJ099131161 (DE-599)DOAJ01a2032629184f729c91c6b4fcf9d0ca DE-627 ger DE-627 rakwb eng QH301-705.5 Wenchao Shi verfasserin aut Bioinformatics approach to identify the hub gene associated with COVID‐19 and idiopathic pulmonary fibrosis 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The coronavirus disease 2019 (COVID‐19) has developed into a global health crisis. Pulmonary fibrosis, as one of the complications of SARS‐CoV‐2 infection, deserves attention. As COVID‐19 is a new clinical entity that is constantly evolving, and many aspects of disease are remain unknown. The datasets of COVID‐19 and idiopathic pulmonary fibrosis were obtained from the Gene Expression Omnibus. The hub genes were screened out using the Random Forest (RF) algorithm depending on the severity of patients with COVID‐19. A risk prediction model was developed to assess the prognosis of patients infected with SARS‐CoV‐2, which was evaluated by another dataset. Six genes (named NELL2, GPR183, S100A8, ALPL, CD177, and IL1R2) may be associated with the development of PF in patients with severe SARS‐CoV‐2 infection. S100A8 is thought to be an important target gene that is closely associated with COVID‐19 and pulmonary fibrosis. Construction of a neural network model was successfully predicted the prognosis of patients with COVID‐19. With the increasing availability of COVID‐19 datasets, bioinformatic methods can provide possible predictive targets for the diagnosis, treatment, and prognosis of the disease and show intervention directions for the development of clinical drugs and vaccines. big data bioinformatics Biology (General) Tinghui Li verfasserin aut Huiwen Li verfasserin aut Juan Ren verfasserin aut Meiyu Lv verfasserin aut Qi Wang verfasserin aut Yaowu He verfasserin aut Yao Yu verfasserin aut Lijie Liu verfasserin aut Shoude Jin verfasserin aut Hong Chen verfasserin aut In IET Systems Biology Wiley, 2021 17(2023), 6, Seite 336-351 (DE-627)521693756 (DE-600)2264538-X 17518857 nnns volume:17 year:2023 number:6 pages:336-351 https://doi.org/10.1049/syb2.12080 kostenfrei https://doaj.org/article/01a2032629184f729c91c6b4fcf9d0ca kostenfrei https://doi.org/10.1049/syb2.12080 kostenfrei https://doaj.org/toc/1751-8849 Journal toc kostenfrei https://doaj.org/toc/1751-8857 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 17 2023 6 336-351 |
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10.1049/syb2.12080 doi (DE-627)DOAJ099131161 (DE-599)DOAJ01a2032629184f729c91c6b4fcf9d0ca DE-627 ger DE-627 rakwb eng QH301-705.5 Wenchao Shi verfasserin aut Bioinformatics approach to identify the hub gene associated with COVID‐19 and idiopathic pulmonary fibrosis 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The coronavirus disease 2019 (COVID‐19) has developed into a global health crisis. Pulmonary fibrosis, as one of the complications of SARS‐CoV‐2 infection, deserves attention. As COVID‐19 is a new clinical entity that is constantly evolving, and many aspects of disease are remain unknown. The datasets of COVID‐19 and idiopathic pulmonary fibrosis were obtained from the Gene Expression Omnibus. The hub genes were screened out using the Random Forest (RF) algorithm depending on the severity of patients with COVID‐19. A risk prediction model was developed to assess the prognosis of patients infected with SARS‐CoV‐2, which was evaluated by another dataset. Six genes (named NELL2, GPR183, S100A8, ALPL, CD177, and IL1R2) may be associated with the development of PF in patients with severe SARS‐CoV‐2 infection. S100A8 is thought to be an important target gene that is closely associated with COVID‐19 and pulmonary fibrosis. Construction of a neural network model was successfully predicted the prognosis of patients with COVID‐19. With the increasing availability of COVID‐19 datasets, bioinformatic methods can provide possible predictive targets for the diagnosis, treatment, and prognosis of the disease and show intervention directions for the development of clinical drugs and vaccines. big data bioinformatics Biology (General) Tinghui Li verfasserin aut Huiwen Li verfasserin aut Juan Ren verfasserin aut Meiyu Lv verfasserin aut Qi Wang verfasserin aut Yaowu He verfasserin aut Yao Yu verfasserin aut Lijie Liu verfasserin aut Shoude Jin verfasserin aut Hong Chen verfasserin aut In IET Systems Biology Wiley, 2021 17(2023), 6, Seite 336-351 (DE-627)521693756 (DE-600)2264538-X 17518857 nnns volume:17 year:2023 number:6 pages:336-351 https://doi.org/10.1049/syb2.12080 kostenfrei https://doaj.org/article/01a2032629184f729c91c6b4fcf9d0ca kostenfrei https://doi.org/10.1049/syb2.12080 kostenfrei https://doaj.org/toc/1751-8849 Journal toc kostenfrei https://doaj.org/toc/1751-8857 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 17 2023 6 336-351 |
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10.1049/syb2.12080 doi (DE-627)DOAJ099131161 (DE-599)DOAJ01a2032629184f729c91c6b4fcf9d0ca DE-627 ger DE-627 rakwb eng QH301-705.5 Wenchao Shi verfasserin aut Bioinformatics approach to identify the hub gene associated with COVID‐19 and idiopathic pulmonary fibrosis 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The coronavirus disease 2019 (COVID‐19) has developed into a global health crisis. Pulmonary fibrosis, as one of the complications of SARS‐CoV‐2 infection, deserves attention. As COVID‐19 is a new clinical entity that is constantly evolving, and many aspects of disease are remain unknown. The datasets of COVID‐19 and idiopathic pulmonary fibrosis were obtained from the Gene Expression Omnibus. The hub genes were screened out using the Random Forest (RF) algorithm depending on the severity of patients with COVID‐19. A risk prediction model was developed to assess the prognosis of patients infected with SARS‐CoV‐2, which was evaluated by another dataset. Six genes (named NELL2, GPR183, S100A8, ALPL, CD177, and IL1R2) may be associated with the development of PF in patients with severe SARS‐CoV‐2 infection. S100A8 is thought to be an important target gene that is closely associated with COVID‐19 and pulmonary fibrosis. Construction of a neural network model was successfully predicted the prognosis of patients with COVID‐19. With the increasing availability of COVID‐19 datasets, bioinformatic methods can provide possible predictive targets for the diagnosis, treatment, and prognosis of the disease and show intervention directions for the development of clinical drugs and vaccines. big data bioinformatics Biology (General) Tinghui Li verfasserin aut Huiwen Li verfasserin aut Juan Ren verfasserin aut Meiyu Lv verfasserin aut Qi Wang verfasserin aut Yaowu He verfasserin aut Yao Yu verfasserin aut Lijie Liu verfasserin aut Shoude Jin verfasserin aut Hong Chen verfasserin aut In IET Systems Biology Wiley, 2021 17(2023), 6, Seite 336-351 (DE-627)521693756 (DE-600)2264538-X 17518857 nnns volume:17 year:2023 number:6 pages:336-351 https://doi.org/10.1049/syb2.12080 kostenfrei https://doaj.org/article/01a2032629184f729c91c6b4fcf9d0ca kostenfrei https://doi.org/10.1049/syb2.12080 kostenfrei https://doaj.org/toc/1751-8849 Journal toc kostenfrei https://doaj.org/toc/1751-8857 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 17 2023 6 336-351 |
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10.1049/syb2.12080 doi (DE-627)DOAJ099131161 (DE-599)DOAJ01a2032629184f729c91c6b4fcf9d0ca DE-627 ger DE-627 rakwb eng QH301-705.5 Wenchao Shi verfasserin aut Bioinformatics approach to identify the hub gene associated with COVID‐19 and idiopathic pulmonary fibrosis 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The coronavirus disease 2019 (COVID‐19) has developed into a global health crisis. Pulmonary fibrosis, as one of the complications of SARS‐CoV‐2 infection, deserves attention. As COVID‐19 is a new clinical entity that is constantly evolving, and many aspects of disease are remain unknown. The datasets of COVID‐19 and idiopathic pulmonary fibrosis were obtained from the Gene Expression Omnibus. The hub genes were screened out using the Random Forest (RF) algorithm depending on the severity of patients with COVID‐19. A risk prediction model was developed to assess the prognosis of patients infected with SARS‐CoV‐2, which was evaluated by another dataset. Six genes (named NELL2, GPR183, S100A8, ALPL, CD177, and IL1R2) may be associated with the development of PF in patients with severe SARS‐CoV‐2 infection. S100A8 is thought to be an important target gene that is closely associated with COVID‐19 and pulmonary fibrosis. Construction of a neural network model was successfully predicted the prognosis of patients with COVID‐19. With the increasing availability of COVID‐19 datasets, bioinformatic methods can provide possible predictive targets for the diagnosis, treatment, and prognosis of the disease and show intervention directions for the development of clinical drugs and vaccines. big data bioinformatics Biology (General) Tinghui Li verfasserin aut Huiwen Li verfasserin aut Juan Ren verfasserin aut Meiyu Lv verfasserin aut Qi Wang verfasserin aut Yaowu He verfasserin aut Yao Yu verfasserin aut Lijie Liu verfasserin aut Shoude Jin verfasserin aut Hong Chen verfasserin aut In IET Systems Biology Wiley, 2021 17(2023), 6, Seite 336-351 (DE-627)521693756 (DE-600)2264538-X 17518857 nnns volume:17 year:2023 number:6 pages:336-351 https://doi.org/10.1049/syb2.12080 kostenfrei https://doaj.org/article/01a2032629184f729c91c6b4fcf9d0ca kostenfrei https://doi.org/10.1049/syb2.12080 kostenfrei https://doaj.org/toc/1751-8849 Journal toc kostenfrei https://doaj.org/toc/1751-8857 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 17 2023 6 336-351 |
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Wenchao Shi @@aut@@ Tinghui Li @@aut@@ Huiwen Li @@aut@@ Juan Ren @@aut@@ Meiyu Lv @@aut@@ Qi Wang @@aut@@ Yaowu He @@aut@@ Yao Yu @@aut@@ Lijie Liu @@aut@@ Shoude Jin @@aut@@ Hong Chen @@aut@@ |
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Pulmonary fibrosis, as one of the complications of SARS‐CoV‐2 infection, deserves attention. As COVID‐19 is a new clinical entity that is constantly evolving, and many aspects of disease are remain unknown. The datasets of COVID‐19 and idiopathic pulmonary fibrosis were obtained from the Gene Expression Omnibus. The hub genes were screened out using the Random Forest (RF) algorithm depending on the severity of patients with COVID‐19. A risk prediction model was developed to assess the prognosis of patients infected with SARS‐CoV‐2, which was evaluated by another dataset. Six genes (named NELL2, GPR183, S100A8, ALPL, CD177, and IL1R2) may be associated with the development of PF in patients with severe SARS‐CoV‐2 infection. S100A8 is thought to be an important target gene that is closely associated with COVID‐19 and pulmonary fibrosis. Construction of a neural network model was successfully predicted the prognosis of patients with COVID‐19. 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bioinformatics approach to identify the hub gene associated with covid‐19 and idiopathic pulmonary fibrosis |
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Bioinformatics approach to identify the hub gene associated with COVID‐19 and idiopathic pulmonary fibrosis |
abstract |
Abstract The coronavirus disease 2019 (COVID‐19) has developed into a global health crisis. Pulmonary fibrosis, as one of the complications of SARS‐CoV‐2 infection, deserves attention. As COVID‐19 is a new clinical entity that is constantly evolving, and many aspects of disease are remain unknown. The datasets of COVID‐19 and idiopathic pulmonary fibrosis were obtained from the Gene Expression Omnibus. The hub genes were screened out using the Random Forest (RF) algorithm depending on the severity of patients with COVID‐19. A risk prediction model was developed to assess the prognosis of patients infected with SARS‐CoV‐2, which was evaluated by another dataset. Six genes (named NELL2, GPR183, S100A8, ALPL, CD177, and IL1R2) may be associated with the development of PF in patients with severe SARS‐CoV‐2 infection. S100A8 is thought to be an important target gene that is closely associated with COVID‐19 and pulmonary fibrosis. Construction of a neural network model was successfully predicted the prognosis of patients with COVID‐19. With the increasing availability of COVID‐19 datasets, bioinformatic methods can provide possible predictive targets for the diagnosis, treatment, and prognosis of the disease and show intervention directions for the development of clinical drugs and vaccines. |
abstractGer |
Abstract The coronavirus disease 2019 (COVID‐19) has developed into a global health crisis. Pulmonary fibrosis, as one of the complications of SARS‐CoV‐2 infection, deserves attention. As COVID‐19 is a new clinical entity that is constantly evolving, and many aspects of disease are remain unknown. The datasets of COVID‐19 and idiopathic pulmonary fibrosis were obtained from the Gene Expression Omnibus. The hub genes were screened out using the Random Forest (RF) algorithm depending on the severity of patients with COVID‐19. A risk prediction model was developed to assess the prognosis of patients infected with SARS‐CoV‐2, which was evaluated by another dataset. Six genes (named NELL2, GPR183, S100A8, ALPL, CD177, and IL1R2) may be associated with the development of PF in patients with severe SARS‐CoV‐2 infection. S100A8 is thought to be an important target gene that is closely associated with COVID‐19 and pulmonary fibrosis. Construction of a neural network model was successfully predicted the prognosis of patients with COVID‐19. With the increasing availability of COVID‐19 datasets, bioinformatic methods can provide possible predictive targets for the diagnosis, treatment, and prognosis of the disease and show intervention directions for the development of clinical drugs and vaccines. |
abstract_unstemmed |
Abstract The coronavirus disease 2019 (COVID‐19) has developed into a global health crisis. Pulmonary fibrosis, as one of the complications of SARS‐CoV‐2 infection, deserves attention. As COVID‐19 is a new clinical entity that is constantly evolving, and many aspects of disease are remain unknown. The datasets of COVID‐19 and idiopathic pulmonary fibrosis were obtained from the Gene Expression Omnibus. The hub genes were screened out using the Random Forest (RF) algorithm depending on the severity of patients with COVID‐19. A risk prediction model was developed to assess the prognosis of patients infected with SARS‐CoV‐2, which was evaluated by another dataset. Six genes (named NELL2, GPR183, S100A8, ALPL, CD177, and IL1R2) may be associated with the development of PF in patients with severe SARS‐CoV‐2 infection. S100A8 is thought to be an important target gene that is closely associated with COVID‐19 and pulmonary fibrosis. Construction of a neural network model was successfully predicted the prognosis of patients with COVID‐19. With the increasing availability of COVID‐19 datasets, bioinformatic methods can provide possible predictive targets for the diagnosis, treatment, and prognosis of the disease and show intervention directions for the development of clinical drugs and vaccines. |
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title_short |
Bioinformatics approach to identify the hub gene associated with COVID‐19 and idiopathic pulmonary fibrosis |
url |
https://doi.org/10.1049/syb2.12080 https://doaj.org/article/01a2032629184f729c91c6b4fcf9d0ca https://doaj.org/toc/1751-8849 https://doaj.org/toc/1751-8857 |
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Tinghui Li Huiwen Li Juan Ren Meiyu Lv Qi Wang Yaowu He Yao Yu Lijie Liu Shoude Jin Hong Chen |
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Tinghui Li Huiwen Li Juan Ren Meiyu Lv Qi Wang Yaowu He Yao Yu Lijie Liu Shoude Jin Hong Chen |
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521693756 |
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QH - Natural History and Biology |
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10.1049/syb2.12080 |
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up_date |
2024-07-03T21:09:49.267Z |
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score |
7.4018383 |