In a recent study, we conducted a probabilistic inference of modulator activity on RelA activity using experimentally determined protein-protein interactions integrated with ChIP-Seq and a compendium of microarray data.
Alvarez, M. J. et al. Functional characterization of somatic mutations in cancer using network-based inference of protein activity.
Alvarez MJ, Shen Y, Giorgi FM, Lachmann A, Ding BB, Ye BH, Califano A. Functional characterization of somatic mutations in cancer using network-based inference of protein activity.
The unprecedented inference of pharmacological activity across GPCR families provides proof-of-concept for in silico approaches against Family C targets based on Family A templates, greatly expanding the prospects of successful drug design and discovery.
Based on knowledge gained by modeling networks of molecular interactions in the cell, their computational algorithm, called VIPER (Virtual Inference of Protein activity by Enriched Regulon analysis), offers a unique new strategy for understanding how cancer cells survive and for identifying personalized cancer therapeutics.
Towards that end, algorithms such as Virtual Inference of Protein Activity by Enriched Regulon Analysis (VIPER) are being used for the assessment of aberrant oncogene expression [56].
Nonetheless, our method achieves accurate inference of miRNA activity modification in the miRNA transfection data as shown above.
Recently, Su et al. [ 11] proposed a classification method based on probabilistic inference of pathway activity.
Without such an understanding, inference of spiking activity from the BOLD signal may be seriously misguided.
The lack of post-translational information in our models represents a limitation in the inference of TF activity.
