Improved classification of mass spectrometry database search results using newer machine learning approaches.
Mol Cell Proteomics
; 5(3): 497-509, 2006 Mar.
Article
en En
| MEDLINE
| ID: mdl-16321970
ABSTRACT
Manual analysis of mass spectrometry data is a current bottleneck in high throughput proteomics. In particular, the need to manually validate the results of mass spectrometry database searching algorithms can be prohibitively time-consuming. Development of software tools that attempt to quantify the confidence in the assignment of a protein or peptide identity to a mass spectrum is an area of active interest. We sought to extend work in this area by investigating the potential of recent machine learning algorithms to improve the accuracy of these approaches and as a flexible framework for accommodating new data features. Specifically we demonstrated the ability of boosting and random forest approaches to improve the discrimination of true hits from false positive identifications in the results of mass spectrometry database search engines compared with thresholding and other machine learning approaches. We accommodated additional attributes obtainable from database search results, including a factor addressing proton mobility. Performance was evaluated using publically available electrospray data and a new collection of MALDI data generated from purified human reference proteins.
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Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Péptidos
/
Inteligencia Artificial
/
Espectrometría de Masa por Láser de Matriz Asistida de Ionización Desorción
/
Biología Computacional
/
Bases de Datos como Asunto
/
Proteómica
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Mol Cell Proteomics
Asunto de la revista:
BIOLOGIA MOLECULAR
/
BIOQUIMICA
Año:
2006
Tipo del documento:
Article
País de afiliación:
Estados Unidos