About: 2L-PCA: a two-level principal component analyzer for quantitative drug design and its applications

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	2L-PCA: a two-level principal component analyzer for quantitative drug design and its applications
Creator	Chou, Kuo-Chen Wang, Shu-Qing Du, Qi-Shi Huang, Ri-Bo Wang, Qing-Yan Xie, Neng-Zhong
Source	PMC
abstract	A two-level principal component predictor (2L-PCA) was proposed based on the principal component analysis (PCA) approach. It can be used to quantitatively analyze various compounds and peptides about their functions or potentials to become useful drugs. One level is for dealing with the physicochemical properties of drug molecules, while the other level is for dealing with their structural fragments. The predictor has the self-learning and feedback features to automatically improve its accuracy. It is anticipated that 2L-PCA will become a very useful tool for timely providing various useful clues during the process of drug development.
has issue date	2017-08-01(xsd:dateTime)
bibo:doi	10.18632/oncotarget.19757
bibo:pmid	29050302
has license	cc-by
sha1sum (hex)	f580401828d99aa0874fc10a731aa34a8cfb69b3
schema:url	https://doi.org/10.18632/oncotarget.19757
resource representing a document's title	2L-PCA: a two-level principal component analyzer for quantitative drug design and its applications
has PubMed Central identifier	PMC5642577
has PubMed identifier	29050302
schema:publication	Oncotarget
resource representing a document's body	covid:f580401828d99aa0874fc10a731aa34a8cfb69b3#body_text
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