Current trends in machine-learning methods applied to spectroscopic cancer diagnosis-Reference-Cited by-同舟云学术

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Current trends in machine-learning methods applied to spectroscopic cancer diagnosis

Published:2014-07 Issue: Volume:59 Page:17-25
ISSN:0165-9936
Container-title:TrAC Trends in Analytical Chemistry
language:en
Short-container-title:TrAC Trends in Analytical Chemistry

Author:

Sattlecker Martina,Stone Nicholas,Bessant Conrad

Funder

Cranfield University and Gloucestershire Hospitals NHS Foundation Trust

NIHR Career Scientist Research Fellowship

Publisher

Elsevier BV

Subject

Spectroscopy,Analytical Chemistry

Reference47 articles.

1. Vibrational spectroscopy: a clinical tool for cancer diagnostics;Kendall;Analyst,2009

2. Reproducibility of the diagnosis of dysplasia in Barrett esophagus: a reaffirmation;Montgomery;Hum. Pathol,2001

3. Chemometrics for Pattern Recognition;Brereton,2009

4. The Elements of Statistical Learning : Data Mining, Inference, and Prediction;Hastie,2009

5. Extracting biological information with computational analysis of Fourier-transform infrared (FTIR) biospectroscopy datasets: current practices to future perspectives;Trevisan;Analyst,2012

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