Machine learning-based data analytic approaches for evaluating post-natal mouse respiratory physiological evolution
• Machine learning-based data analytic approaches help uncover differences in ventilation at later post-natal ages that have not been well appreciated in the past. • Respiratory variability in breathing parameters are key characteristics that separate animals based on post-natal age. • Reliable clas...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wang, Wesley [verfasserIn] |
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E-Artikel |
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Englisch |
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2021transfer abstract |
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Enthalten in: Usage of formal financial services in India: Demand barriers or supply constraints? - Kumar, Abhishek ELSEVIER, 2018, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:283 ; year:2021 ; pages:0 |
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| DOI / URN: |
10.1016/j.resp.2020.103558 |
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| 520 | |a • Machine learning-based data analytic approaches help uncover differences in ventilation at later post-natal ages that have not been well appreciated in the past. • Respiratory variability in breathing parameters are key characteristics that separate animals based on post-natal age. • Reliable class label prediction can be executed by machines utilizing data derived from plethysmography respiratory metrics using various learning pipelines. • The incorporation of machine learning in respiratory physiology is feasible and introduces stronger study rigor and objectivity while permitting high dimensional data analysis. | ||
| 520 | |a • Machine learning-based data analytic approaches help uncover differences in ventilation at later post-natal ages that have not been well appreciated in the past. • Respiratory variability in breathing parameters are key characteristics that separate animals based on post-natal age. • Reliable class label prediction can be executed by machines utilizing data derived from plethysmography respiratory metrics using various learning pipelines. • The incorporation of machine learning in respiratory physiology is feasible and introduces stronger study rigor and objectivity while permitting high dimensional data analysis. | ||
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10.1016/j.resp.2020.103558 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001262.pica (DE-627)ELV051970872 (ELSEVIER)S1569-9048(20)30216-0 DE-627 ger DE-627 rakwb eng 330 VZ Wang, Wesley verfasserin aut Machine learning-based data analytic approaches for evaluating post-natal mouse respiratory physiological evolution 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • Machine learning-based data analytic approaches help uncover differences in ventilation at later post-natal ages that have not been well appreciated in the past. • Respiratory variability in breathing parameters are key characteristics that separate animals based on post-natal age. • Reliable class label prediction can be executed by machines utilizing data derived from plethysmography respiratory metrics using various learning pipelines. • The incorporation of machine learning in respiratory physiology is feasible and introduces stronger study rigor and objectivity while permitting high dimensional data analysis. • Machine learning-based data analytic approaches help uncover differences in ventilation at later post-natal ages that have not been well appreciated in the past. • Respiratory variability in breathing parameters are key characteristics that separate animals based on post-natal age. • Reliable class label prediction can be executed by machines utilizing data derived from plethysmography respiratory metrics using various learning pipelines. • The incorporation of machine learning in respiratory physiology is feasible and introduces stronger study rigor and objectivity while permitting high dimensional data analysis. TV Elsevier f Elsevier Alzate-Correa, Diego oth Alves, Michele Joana oth Jones, Mikayla oth Garcia, Alfredo J. oth Zhao, Jing oth Czeisler, Catherine Miriam oth Otero, José Javier oth Enthalten in Elsevier Science Kumar, Abhishek ELSEVIER Usage of formal financial services in India: Demand barriers or supply constraints? 2018 Amsterdam [u.a.] (DE-627)ELV002357348 volume:283 year:2021 pages:0 https://doi.org/10.1016/j.resp.2020.103558 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 283 2021 0 |
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10.1016/j.resp.2020.103558 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001262.pica (DE-627)ELV051970872 (ELSEVIER)S1569-9048(20)30216-0 DE-627 ger DE-627 rakwb eng 330 VZ Wang, Wesley verfasserin aut Machine learning-based data analytic approaches for evaluating post-natal mouse respiratory physiological evolution 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • Machine learning-based data analytic approaches help uncover differences in ventilation at later post-natal ages that have not been well appreciated in the past. • Respiratory variability in breathing parameters are key characteristics that separate animals based on post-natal age. • Reliable class label prediction can be executed by machines utilizing data derived from plethysmography respiratory metrics using various learning pipelines. • The incorporation of machine learning in respiratory physiology is feasible and introduces stronger study rigor and objectivity while permitting high dimensional data analysis. • Machine learning-based data analytic approaches help uncover differences in ventilation at later post-natal ages that have not been well appreciated in the past. • Respiratory variability in breathing parameters are key characteristics that separate animals based on post-natal age. • Reliable class label prediction can be executed by machines utilizing data derived from plethysmography respiratory metrics using various learning pipelines. • The incorporation of machine learning in respiratory physiology is feasible and introduces stronger study rigor and objectivity while permitting high dimensional data analysis. TV Elsevier f Elsevier Alzate-Correa, Diego oth Alves, Michele Joana oth Jones, Mikayla oth Garcia, Alfredo J. oth Zhao, Jing oth Czeisler, Catherine Miriam oth Otero, José Javier oth Enthalten in Elsevier Science Kumar, Abhishek ELSEVIER Usage of formal financial services in India: Demand barriers or supply constraints? 2018 Amsterdam [u.a.] (DE-627)ELV002357348 volume:283 year:2021 pages:0 https://doi.org/10.1016/j.resp.2020.103558 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 283 2021 0 |
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10.1016/j.resp.2020.103558 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001262.pica (DE-627)ELV051970872 (ELSEVIER)S1569-9048(20)30216-0 DE-627 ger DE-627 rakwb eng 330 VZ Wang, Wesley verfasserin aut Machine learning-based data analytic approaches for evaluating post-natal mouse respiratory physiological evolution 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • Machine learning-based data analytic approaches help uncover differences in ventilation at later post-natal ages that have not been well appreciated in the past. • Respiratory variability in breathing parameters are key characteristics that separate animals based on post-natal age. • Reliable class label prediction can be executed by machines utilizing data derived from plethysmography respiratory metrics using various learning pipelines. • The incorporation of machine learning in respiratory physiology is feasible and introduces stronger study rigor and objectivity while permitting high dimensional data analysis. • Machine learning-based data analytic approaches help uncover differences in ventilation at later post-natal ages that have not been well appreciated in the past. • Respiratory variability in breathing parameters are key characteristics that separate animals based on post-natal age. • Reliable class label prediction can be executed by machines utilizing data derived from plethysmography respiratory metrics using various learning pipelines. • The incorporation of machine learning in respiratory physiology is feasible and introduces stronger study rigor and objectivity while permitting high dimensional data analysis. TV Elsevier f Elsevier Alzate-Correa, Diego oth Alves, Michele Joana oth Jones, Mikayla oth Garcia, Alfredo J. oth Zhao, Jing oth Czeisler, Catherine Miriam oth Otero, José Javier oth Enthalten in Elsevier Science Kumar, Abhishek ELSEVIER Usage of formal financial services in India: Demand barriers or supply constraints? 2018 Amsterdam [u.a.] (DE-627)ELV002357348 volume:283 year:2021 pages:0 https://doi.org/10.1016/j.resp.2020.103558 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 283 2021 0 |
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10.1016/j.resp.2020.103558 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001262.pica (DE-627)ELV051970872 (ELSEVIER)S1569-9048(20)30216-0 DE-627 ger DE-627 rakwb eng 330 VZ Wang, Wesley verfasserin aut Machine learning-based data analytic approaches for evaluating post-natal mouse respiratory physiological evolution 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • Machine learning-based data analytic approaches help uncover differences in ventilation at later post-natal ages that have not been well appreciated in the past. • Respiratory variability in breathing parameters are key characteristics that separate animals based on post-natal age. • Reliable class label prediction can be executed by machines utilizing data derived from plethysmography respiratory metrics using various learning pipelines. • The incorporation of machine learning in respiratory physiology is feasible and introduces stronger study rigor and objectivity while permitting high dimensional data analysis. • Machine learning-based data analytic approaches help uncover differences in ventilation at later post-natal ages that have not been well appreciated in the past. • Respiratory variability in breathing parameters are key characteristics that separate animals based on post-natal age. • Reliable class label prediction can be executed by machines utilizing data derived from plethysmography respiratory metrics using various learning pipelines. • The incorporation of machine learning in respiratory physiology is feasible and introduces stronger study rigor and objectivity while permitting high dimensional data analysis. TV Elsevier f Elsevier Alzate-Correa, Diego oth Alves, Michele Joana oth Jones, Mikayla oth Garcia, Alfredo J. oth Zhao, Jing oth Czeisler, Catherine Miriam oth Otero, José Javier oth Enthalten in Elsevier Science Kumar, Abhishek ELSEVIER Usage of formal financial services in India: Demand barriers or supply constraints? 2018 Amsterdam [u.a.] (DE-627)ELV002357348 volume:283 year:2021 pages:0 https://doi.org/10.1016/j.resp.2020.103558 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 283 2021 0 |
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• Machine learning-based data analytic approaches help uncover differences in ventilation at later post-natal ages that have not been well appreciated in the past. • Respiratory variability in breathing parameters are key characteristics that separate animals based on post-natal age. • Reliable class label prediction can be executed by machines utilizing data derived from plethysmography respiratory metrics using various learning pipelines. • The incorporation of machine learning in respiratory physiology is feasible and introduces stronger study rigor and objectivity while permitting high dimensional data analysis. |
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• Machine learning-based data analytic approaches help uncover differences in ventilation at later post-natal ages that have not been well appreciated in the past. • Respiratory variability in breathing parameters are key characteristics that separate animals based on post-natal age. • Reliable class label prediction can be executed by machines utilizing data derived from plethysmography respiratory metrics using various learning pipelines. • The incorporation of machine learning in respiratory physiology is feasible and introduces stronger study rigor and objectivity while permitting high dimensional data analysis. |
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• Machine learning-based data analytic approaches help uncover differences in ventilation at later post-natal ages that have not been well appreciated in the past. • Respiratory variability in breathing parameters are key characteristics that separate animals based on post-natal age. • Reliable class label prediction can be executed by machines utilizing data derived from plethysmography respiratory metrics using various learning pipelines. • The incorporation of machine learning in respiratory physiology is feasible and introduces stronger study rigor and objectivity while permitting high dimensional data analysis. |
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