NIM molecular network tool (NIMNT) takes protein data or mRNA data as input, mapping them onto a large NIM background interactome network with direction (activation, inhibition, unspecified) and known mechanisms including binding, receptor binding, transcription regulation, Phosphorylation, co-regulation of transcription, sulfation etc. Based on the background network, it uses the Prize-Collecting Steiner Forest (PCSF) algorithm to extract a high-confidence subnetwork relevant to your data. This can be used to extract the condition-specific NIM subnetworks, such as subnetworks under the disturbance of diseases or drugs, which can help identify cellular pathways and other proteins that are involved in your experimental system. Then we can use the subnetworks for further study (Prediction of disease targets and biomarkers, Interpretation of the pathogenesis of disease, and Prediction of synergistic effects of drugs, and so forth).
This Sample files:Sample prize file
NIMNT is supported by a grant from National Nature Science Foundation of China.
Copyright © 2017 State Key Laboratory of Toxicology and Medical Countermeasures, Beijing Institute of Pharmacology and Toxicology.
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