Predictive model performance for FTIR spectra.

Predictive model performance for FTIR spectra.

<p>Bootstrap estimation of AUROC, sensitivity and specificity of the elastic net classifier trained on the DC-transformed data.</p>

محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Sarah Malek (342549) (author)
مؤلفون آخرون:	Roman M. Natoli (22086357) (author), Bartek Rajwa (127236) (author)
منشور في:	2025
الموضوعات:	Biochemistry Microbiology Cell Biology Genetics Molecular Biology Biotechnology Developmental Biology Cancer Infectious Diseases Chemical Sciences not elsewhere classified receiver operating characteristic protein abundance ratios develop predictive models candidate diagnostic biomarkers predictive models based based predictive models fourier transform infrared spectroscopy &# 160 proteomic mass spectrometry ftir spectroscopy data xlink "> fracture xlink "> mass spectrometry ftir spectroscopy based spectra infrared spectra &# 8776 significant impact related infections quantitative approach qualitative approach prospective case patient outcomes multivariate analysis ms data fracture region early stages dried films developed separately challenging due batch analysis average sensitivity average area analytical approaches 81 ), 76 ), 747 ), 737 ), 682 ).
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الوصف
الملخص:	<p>Bootstrap estimation of AUROC, sensitivity and specificity of the elastic net classifier trained on the DC-transformed data.</p>

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