Employing decomposable partially observable Markov decision processes to control gene regulatory networks

Date

2017-11-01

Author

Erdogdu, Utku
Polat, Faruk
Alhajj, Reda

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

226
views

0
downloads

Objective: Formulate the induction and control of gene regulatory networks (GRNs) from gene expression data using Partially Observable Markov Decision Processes (POMDPs).

Subject Keywords

Gene regulatory networks, Control of gene regulatory networks, Processes, Partially Observable Markov Decision

URI

https://hdl.handle.net/11511/44975

Journal

ARTIFICIAL INTELLIGENCE IN MEDICINE

DOI

https://doi.org/10.1016/j.artmed.2017.06.007

Collections

Department of Computer Engineering, Article

Suggestions

OpenMETU
Core

Integer linear programming based solutions for construction of biological networks Eren Özsoy, Öykü; Can, Tolga; Department of Health Informatics (2014) Inference of gene regulatory or signaling networks from perturbation experiments and gene expression assays is one of the challenging problems in bioinformatics. Recently, the inference problem has been formulated as a reference network editing problem and it has been show that finding the minimum number of edit operations on a reference network in order to comply with perturbation experiments is an NP-complete problem. In this dissertation, we propose linear programming based solutions for reconstruction o...
Partially Observable Gene Regulatory Network Control Without a Boundary on Horizon Erdogdu, Utku; Polat, Faruk; Alhajj, Reda (2012-11-09) Gene regulatory networks (GRNs) govern the protein transcription process in the cell and interactions among genes play a vital role in determining the biosynthesis rate of proteins. By using intervention techniques discovered by biological research it is possible to control a GRN, thus promoting or demoting the expression rate of a certain gene. In this work, this control task is studied in a partially observable setting where interventions lack perfect knowledge of the expression level of all genes. Moreov...
Mathematical Modeling and Approximation of Gene Expression Patterns Yılmaz, Fatih; Öktem, Hüseyin Avni (2004-09-03) This study concerns modeling, approximation and inference of gene regulatory dynamics on the basis of gene expression patterns. The dynamical behavior of gene expressions is represented by a system of ordinary differential equations. We introduce a gene-interaction matrix with some nonlinear entries, in particular, quadratic polynomials of the expression levels to keep the system solvable. The model parameters are determined by using optimization. Then, we provide the time-discrete approximation of our time...
Data Integration in Functional Analysis of MicroRNAs OĞUL, HASAN; Akkaya, Mahinur (2011-12-01) The discovery of microRNAs (miRNAs), about a decade ago, has completely changed our understanding of the complexity of gene regulatory networks. It has already been shown that they are abundantly found in many organisms and can regulate hundreds of genes in post-transcriptional level. To elucidate the individual or co-operative effects of miRNAs, it is required to place them in the overall network of gene regulation and link them to other pathways and systems-level processes. One key step in this effort is ...
Large-Scale Approximate Intervention Strategies for Probabilistic Boolean Networks as Models of Gene Regulation Tan, Mehmet; Alhajj, Reda; Polat, Faruk (2008-10-10) Control of Probabilistic Boolean Networks as models of gene regulation is an important problem; the solution may help researchers in various different areas. But as generally applies to control problems, the size of the state space in gene regulatory networks is too large to be considered for comprehensive solution to the problem; this is evident from the work done in the field, where only very small portions of the whole genome of an organism could be used in control applications. The Factored Markov Decis...

Citation Formats

U. Erdogdu, F. Polat, and R. Alhajj, “Employing decomposable partially observable Markov decision processes to control gene regulatory networks,” ARTIFICIAL INTELLIGENCE IN MEDICINE, pp. 14–34, 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/44975.