Prediction and Characterization of Missing Proteomic Data in Desulfovibrio vulgaris.
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ABSTRACT:
SUBMITTER: Li F
PROVIDER: S-EPMC3114432 | biostudies-literature | 2011
REPOSITORIES: biostudies-literature
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Prediction and Characterization of Missing Proteomic Data in Desulfovibrio vulgaris.
Li Feng F Nie Lei L Wu Gang G Qiao Jianjun J Zhang Weiwen W
Comparative and functional genomics 20110504
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 ...[more]
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