Principal component analysis-based unsupervised feature extraction applied to in silico drug discovery for posttraumatic stress disorder-mediated heart disease

BACKGROUND: Feature extraction (FE) is difficult, particularly if there are more features than samples, as small sample numbers often result in biased outcomes or overfitting. Furthermore, multiple sample classes often complicate FE because evaluating performance, which is usual in supervised FE, is...

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Detalles Bibliográficos
Autores principales: Taguchi, Y-h, Iwadate, Mitsuo, Umeyama, Hideaki
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4448281/
https://www.ncbi.nlm.nih.gov/pubmed/25925353
http://dx.doi.org/10.1186/s12859-015-0574-4

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4448281/
https://www.ncbi.nlm.nih.gov/pubmed/25925353
http://dx.doi.org/10.1186/s12859-015-0574-4

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