Abstract
Systems biology aims to unravel the vast network of functional interactions that govern biological systems. To date, the inference of gene interactions from large-scale 'omics data is typically achieved using correlations. We present the hierarchical interaction score (HIS) and show that the HIS outperforms commonly used methods in the inference of functional interactions between genes measured in large-scale experiments, making it a valuable statistic for systems biology.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1089-1094 |
| Number of pages | 6 |
| Journal | Nature Methods |
| Volume | 10 |
| Issue number | 11 |
| DOIs | |
| State | Published - Nov 2013 |
Funding
We would like to acknowledge E.-M. Damm and A. Schmidt for the phosphoproteomics data, A. Patrignani for analysis of the microarray data, Y. Yakimovich for help on the accompanying website, D. Schlaepfer (University of California, San Diego) for the PTK2-rescue cell line, F. Markowetz and X. Wang for help with NEM analysis, and all members of the Pelkmans lab for useful comments on the manuscript. L.P. acknowledges financial support from the SystemsX.ch RTD projects PhosphoNetX and LipidX and the University of Zurich, and B.S. acknowledges financial support from the Swiss National Science Foundation.
ASJC Scopus subject areas
- Biotechnology
- Biochemistry
- Molecular Biology
- Cell Biology