Comparison between Two Different Genetic Network Inferring Models on Expression Profiles in S. cerevisiae
نویسندگان
چکیده
1 Mitsui Knowledge Industry Co., Ltd, Harmony tower 21th Floor, 1-32-2 Honcho, Nakanoku, Tokyo 164-8721, Japan 2 Laboratory for Applied Biological Regulation Technology, Graduate School of Bioresource and Bioenvironmental Sciences, Kyusyu University, Hakozaki 6-10-1, Higashiku, Fukuoka 812-8581, Japan 3 Laboratory for Molecular Gene Technics, Graduate School of Genetic Resources Technology, Kyusyu University, Hakozaki 6-10-1, Higashi-ku, Fukuoka 812-8581, Japan
منابع مشابه
Dynamic Bayesian Networks Modeling for Inferring Genetic Regulatory Networks by Search Strategy: Comparison between Greedy Hill Climbing and MCMC Methods
Using Dynamic Bayesian Networks (DBN) to model genetic regulatory networks from gene expression data is one of the major paradigms for inferring the interactions among genes. Averaging a collection of models for predicting network is desired, rather than relying on a single high scoring model. In this paper, two kinds of model searching approaches are compared, which are Greedy hill-climbing Se...
متن کاملInferring subnetworks from perturbed expression profiles
Genome-wide expression profiles of genetic mutants provide a wide variety of measurements of cellular responses to perturbations. Typical analysis of such data identifies genes affected by perturbation and uses clustering to group genes of similar function. In this paper we discover a finer structure of interactions between genes, such as causality, mediation, activation, and inhibition by usin...
متن کاملInferring modulators of genetic interactions with epistatic nested effects models
Maps of genetic interactions can dissect functional redundancies in cellular networks. Gene expression profiles as high-dimensional molecular readouts of combinatorial perturbations provide a detailed view of genetic interactions, but can be hard to interpret if different gene sets respond in different ways (called mixed epistasis). Here we test the hypothesis that mixed epistasis between a gen...
متن کاملInference of common genetic network using fuzzy adaptive resonance theory associated matrix method.
Inferring genetic networks from gene expression data is the most challenging work in the post-genomic era. However, most studies tend to show their genetic network inference ability by using artificial data. Here, we developed the fuzzy adaptive resonance theory associated matrix (F-ART matrix) method to infer genetic networks and applied it to experimental time series data, which are gene expr...
متن کاملThe Comparison of Credit Risk between Artificial Neural Network and Logistic Regression Models in Tose-Taavon Bank in Guilan
One of the most important issues always facing banks and financial institutes is the issue of credit risk or the possibility of failure in the fulfillment of obligations by applicants who are receiving credit facilities. The considerable number of banks’ delayed loan payments all around the world shows the importance of this issue and the necessary consideration of this topic. Accordingly...
متن کامل