Molecular interaction networks for the analysis of human disease: Utility, limitations, and considerations
| Autor: | Simone S. Li, Sarah-Jane Schramm, Graham J. Mann, Marc R. Wilkins, Apurv Goel, Yee Hwa Yang, Vivek Jayaswal |
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| Rok vydání: | 2013 |
| Předmět: |
Proteome
media_common.quotation_subject Systems biology Context (language use) computer.software_genre Biochemistry Interactome Field (computer science) Human disease Neoplasms Protein Interaction Mapping Data Mining Humans Quality (business) Protein Interaction Maps Sociology Databases Protein Molecular Biology media_common Molecular Sequence Annotation Data science MicroRNAs Data redundancy Data quality RNA Interference Data mining computer |
| Zdroj: | PROTEOMICS. 13:3393-3405 |
| ISSN: | 1615-9853 |
| DOI: | 10.1002/pmic.201200570 |
| Popis: | High-throughput '-omics' data can be combined with large-scale molecular interaction networks, for example, protein-protein interaction networks, to provide a unique framework for the investigation of human molecular biology. Interest in these integrative '-omics' methods is growing rapidly because of their potential to understand complexity and association with disease; such approaches have a focus on associations between phenotype and "network-type." The potential of this research is enticing, yet there remain a series of important considerations. Here, we discuss interaction data selection, data quality, the relative merits of using data from large high-throughput studies versus a meta-database of smaller literature-curated studies, and possible issues of sociological or inspection bias in interaction data. Other work underway, especially international consortia to establish data formats, quality standards and address data redundancy, and the improvements these efforts are making to the field, is also evaluated. We present options for researchers intending to use large-scale molecular interaction networks as a functional context for protein or gene expression data, including microRNAs, especially in the context of human disease. |
| Databáze: | OpenAIRE |
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