A machine learning perspective on the development of clinical decision support systems utilizing mass spectra of blood samples.
Currently, the best way to reduce the mortality of cancer is to detect and treat it in the earliest stages. Technological advances in genomics and proteomics have opened a new realm of methods for early detection that show potential to overcome the drawbacks of current strategies. In particular, pattern analysis of mass spectra of blood samples has attracted attention as an approach to early detection of cancer. Mass spectrometry provides rapid and precise measurements of the sizes and relative abundances of the proteins present in a complex biological/chemical mixture. This article presents a review of the development of clinical decision support systems using mass spectrometry from a machine learning perspective. The literature is reviewed in an explicit machine learning framework, the components of which are preprocessing, feature extraction, feature selection, classifier training, and evaluation.
Shin
Hyunjin
H
Electrical and Computer Engineering Department, The University of Texas at Austin, USA.
Markey
Mia K
MK
D001185:Artificial Intelligence; D014408:Biomarkers, Tumor / Q000097:blood*; D001774:Blood Chemical Analysis / Q000379:methods; D003660:Decision Support Systems, Management; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D006801:Humans; D013058:Mass Spectrometry / Q000379:methods*; D009363:Neoplasm Proteins / Q000097:blood*; D009369:Neoplasms / Q000097:blood* / Q000175:diagnosis*; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors
D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins