Inferring biomolecular interaction networks based on convex optimization
Computational Biology and Chemistry
The Journal of Machine Learning Research
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Inferring gene interaction networks from ISH images via kernelized graphical models
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Hi-index | 3.84 |
Motivation: The analysis of high-throughput experimental data, for example from microarray experiments, is currently seen as a promising way of finding regulatory relationships between genes. Bayesian networks have been suggested for learning gene regulatory networks from observational data. Not all causal relationships can be inferred from correlation data alone. Often several equivalent but different directed graphs explain the data equally well. Intervention experiments where genes are manipulated can help to narrow down the range of possible networks. Results: We describe an active learning algorithm that suggests an optimized sequence of intervention experiments. Simulation experiments show that our selection scheme is better than an unguided choice of interventions in learning the correct network and compares favorably in running time and results with methods based on value of information calculations. Availability: Algorithms are available from the authors on request.