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1 Ergebnisse
1
Identifying Antitubercular Peptides via Deep Forest Archite..:
Lantian Yao
;
Jiahui Guan
;
Wenshuo Li
...
doi:10.1021/acs.analchem.3c04196.s001. , 2024
Link:
https://doi.org/10.1021/acs.analchem.3c04196.s001
RT Journal T1
Identifying Antitubercular Peptides via Deep Forest Architecture with Effective Feature Representation
UL https://suche.suub.uni-bremen.de/peid=base-ftunivoxfordfig:oai:figshare.com:article_25006633&Exemplar=1&LAN=DE A1 Lantian Yao A1 Jiahui Guan A1 Wenshuo Li A1 Chia-Ru Chung A1 Junyang Deng A1 Ying-Chih Chiang A1 Tzong-Yi Lee YR 2024 K1 Science Policy K1 Space Science K1 Biological Sciences not elsewhere classified K1 Information Systems not elsewhere classified K1 shown promising results K1 severe disease caused K1 sequence analysis tasks K1 novel machine learning K1 like cascading structure K1 https :// github K1 https :// awi K1 experimental results show K1 downloadable desktop application K1 deep forest algorithm K1 conventional wet lab K1 provide potential solutions K1 potential treatment K1 software ) K1 significantly accelerate K1 significant threat K1 resistant strains K1 peptide drugs K1 often fail K1 neural network K1 mycobacterium tuberculosis K1 human health K1 global fight K1 freely available K1 excellent interpretability K1 effectively process K1 discover peptides K1 desired properties K1 data sets K1 critical features K1 based approaches K1 art performance K1 antituberculosis peptides K1 achieving state JF doi:10.1021/acs.analchem.3c04196.s001 LK http://dx.doi.org/https://doi.org/10.1021/acs.analchem.3c04196.s001 DO https://doi.org/10.1021/acs.analchem.3c04196.s001 SF ELIB - SuUB Bremen
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