Reverse Engineering and Analysis of Genome-Wide Gene Regulatory Networks from Gene Expression Profiles Using High-Performance Computing
Graph Chatbot
Chat with Graph Search
Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.
DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.
Background: Identifying large gene regulatory networks is an important task, while the acquisition of data through perturbation experiments (e.g., gene switches, RNAi, heterozygotes) is expensive. It is thus desirable to use an identification ...
The peroxisome proliferator activated receptor gamma (PPARgamma) is a member in the nuclear receptor superfamily which mediates part of the regulatory effects of dietary fatty acids on gene expression. As PPARgamma also coordinates adipocyte differentiatio ...
For better understanding of genetic mechanisms underlying clinical observations, it is interesting to determine which genes and clinical traits are interrelated. In the last few years a consistent amount of research in genomics has been done concerning cor ...
The zebrafish (Danio rerio) embryo toxicity test (DarT) is under consideration as an alternative to the acute fish toxicity test. Microscopically visible developmental disorders or death are the endpoints used to report on toxicity in DarT. These endpoints ...
The effective reverse engineering of biochemical networks is one of the great challenges of systems biology. The contribution of this paper is two-fold: 1) We introduce a new method for reverse engineering genetic regulatory networks from gene expression d ...
In silico modeling of Gene Regulatory Networks has gained a lot of attention recently as it gives a very powerful tool to experimental biologists to gather the knowledge gained from different biological experiments and understand the dynamics of the overal ...
We consider computationally reconstructing gene regulatory networks on top of the binary abstraction of gene expression state information. Unlike previous Boolean network approaches, the proposed method does not handle noisy gene expression values directly ...
The sparse linear model has seen many successful applications in Statistics, Machine Learning, and Computational Biology, such as identification of gene regulatory networks from micro-array expression data. Prior work has either approximated Bayesian infer ...
One of the most important advances in biology in recent years may be the discovery of RNAs that can regulate gene expression. As one kind of such functional noncoding RNAs, microRNAs (miRNAs) forma class of endogenous 19–23-nucleotide RNAs that can have im ...
This paper considers piecewise affine models of genetic regulatory networks and focuses on the problem of detecting switches among different modes of operation in gene expression data. This task constitutes the first step of a procedure for the complete id ...