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Prostate cancer recognition based on mass spectrometry sensing data and data fingerprint recovery
The high dimensionality and noisy spectra of Mass Spectrometry (MS) data are two of the main challenges to achieving high accuracy recognition. The objective of this work is to produce an accurate prediction of class content by employing compressive sensing (CS). Not only can CS significantly reduce...
Αποθηκεύτηκε σε:
| Τόπος έκδοσης: | Biomed Signal Process Control |
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| Κύριοι συγγραφείς: | , , |
| Μορφή: | Artigo |
| Γλώσσα: | Inglês |
| Έκδοση: |
2017
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| Θέματα: | |
| Διαθέσιμο Online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5621758/ https://ncbi.nlm.nih.gov/pubmed/28970861 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1016/j.bspc.2016.12.003 |
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