Prediction and Characterization of Missing Proteomic Data in Desulfovibrio vulgaris

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 s...

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Detalles Bibliográficos
Autores principales: Li, Feng, Nie, Lei, Wu, Gang, Qiao, Jianjun, Zhang, Weiwen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2011
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3114432/
https://www.ncbi.nlm.nih.gov/pubmed/21687592
http://dx.doi.org/10.1155/2011/780973

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3114432/
https://www.ncbi.nlm.nih.gov/pubmed/21687592
http://dx.doi.org/10.1155/2011/780973

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