| Autor: |
R. F. Silva, André, Lima, Diogo B., Leyva, Alejandro, Duran, Rosario, Batthyany, Carlos, Aquino, Priscila F., Leal, Juliana C., Rodriguez, Jimmy E., Domont, Gilberto B., Santos, Marlon D. M., Chamot-Rooke, Julia, Barbosa, Valmir C., Carvalho, Paulo C. |
| Předmět: |
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| Zdroj: |
Bioinformatics; 6/15/2017, Vol. 33 Issue 12, p1883-1885, 3p |
| Abstrakt: |
Motivation: Around 75% of all mass spectra remain unidentified by widely adopted proteomic strategies. We present DiagnoProt, an integrated computational environment that can efficiently cluster millions of spectra and use machine learning to shortlist high-quality unidentified mass spectra that are discriminative of different biological conditions. Results: We exemplify the use of DiagnoProt by shortlisting 4366 high-quality unidentified tandem mass spectra that are discriminative of different types of the Aspergillus fungus. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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