Deeper investigation into the utility of functional class scoring in missing protein prediction from proteomics data.
J Bioinform Comput Biol
; 17(2): 1950013, 2019 04.
Article
en En
| MEDLINE
| ID: mdl-31057071
ABSTRACT
Functional Class Scoring (FCS) is a network-based approach previously demonstrated to be powerful in missing protein prediction (MPP). We update its performance evaluation using data derived from new proteomics technology (SWATH) and also checked for reproducibility using two independent datasets profiling kidney tissue proteome. We also evaluated the objectivity of the FCS p-value, and followed up on the value of MPP from predicted complexes. Our results suggest that (1) FCS p -values are non-objective, and are confounded strongly by complex size, (2) best recovery performance do not necessarily lie at standard p -value cutoffs, (3) while predicted complexes may be used for augmenting MPP, they are inferior to real complexes, and are further confounded by issues relating to network coverage and quality and (4) moderate sized complexes of size 5 to 10 still exhibit considerable instability, we find that FCS works best with big complexes. While FCS is a powerful approach, blind reliance on its non-objective p -value is ill-advised.
Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Biología Computacional
/
Proteómica
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
J Bioinform Comput Biol
Asunto de la revista:
BIOLOGIA
/
INFORMATICA MEDICA
Año:
2019
Tipo del documento:
Article