Prediction and Characterization of Missing Proteomic Data in Desulfovibrio vulgaris.

ABSTRACT: Proteomic datasets are often incomplete due to identification range and sensitivity issues. It becomes important to develop methodologies to estimate missing proteomic data, allowing better interpretation of proteomic datasets and metabolic mechanisms underlying complex biological systems. In this study, we applied an artificial neural network to approximate the relationships between cognate transcriptomic and proteomic datasets of Desulfovibrio vulgaris, and to predict protein abundance for the proteins not experimentally detected, based on several relevant predictors, such as mRNA abundance, cellular role and triple codon counts. The results showed that the coefficients of determination for the trained neural network models ranged from 0.47 to 0.68, providing better modeling than several

SUBMITTER: Li F 

PROVIDER: S-EPMC3114432 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature