29,382 research outputs found
How to understand the cell by breaking it: network analysis of gene perturbation screens
Modern high-throughput gene perturbation screens are key technologies at the
forefront of genetic research. Combined with rich phenotypic descriptors they
enable researchers to observe detailed cellular reactions to experimental
perturbations on a genome-wide scale. This review surveys the current
state-of-the-art in analyzing perturbation screens from a network point of
view. We describe approaches to make the step from the parts list to the wiring
diagram by using phenotypes for network inference and integrating them with
complementary data sources. The first part of the review describes methods to
analyze one- or low-dimensional phenotypes like viability or reporter activity;
the second part concentrates on high-dimensional phenotypes showing global
changes in cell morphology, transcriptome or proteome.Comment: Review based on ISMB 2009 tutorial; after two rounds of revisio
A Posterior Probability Approach for Gene Regulatory Network Inference in Genetic Perturbation Data
Inferring gene regulatory networks is an important problem in systems
biology. However, these networks can be hard to infer from experimental data
because of the inherent variability in biological data as well as the large
number of genes involved. We propose a fast, simple method for inferring
regulatory relationships between genes from knockdown experiments in the NIH
LINCS dataset by calculating posterior probabilities, incorporating prior
information. We show that the method is able to find previously identified
edges from TRANSFAC and JASPAR and discuss the merits and limitations of this
approach
Data based identification and prediction of nonlinear and complex dynamical systems
We thank Dr. R. Yang (formerly at ASU), Dr. R.-Q. Su (formerly at ASU), and Mr. Zhesi Shen for their contributions to a number of original papers on which this Review is partly based. This work was supported by ARO under Grant No. W911NF-14-1-0504. W.-X. Wang was also supported by NSFC under Grants No. 61573064 and No. 61074116, as well as by the Fundamental Research Funds for the Central Universities, Beijing Nova Programme.Peer reviewedPostprin
Inferring dynamic genetic networks with low order independencies
In this paper, we propose a novel inference method for dynamic genetic
networks which makes it possible to face with a number of time measurements n
much smaller than the number of genes p. The approach is based on the concept
of low order conditional dependence graph that we extend here in the case of
Dynamic Bayesian Networks. Most of our results are based on the theory of
graphical models associated with the Directed Acyclic Graphs (DAGs). In this
way, we define a minimal DAG G which describes exactly the full order
conditional dependencies given the past of the process. Then, to face with the
large p and small n estimation case, we propose to approximate DAG G by
considering low order conditional independencies. We introduce partial qth
order conditional dependence DAGs G(q) and analyze their probabilistic
properties. In general, DAGs G(q) differ from DAG G but still reflect relevant
dependence facts for sparse networks such as genetic networks. By using this
approximation, we set out a non-bayesian inference method and demonstrate the
effectiveness of this approach on both simulated and real data analysis. The
inference procedure is implemented in the R package 'G1DBN' freely available
from the CRAN archive
- …