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

Erdogdu, Utku
Polat, Faruk
Alhajj, Reda
Objective: Formulate the induction and control of gene regulatory networks (GRNs) from gene expression data using Partially Observable Markov Decision Processes (POMDPs).


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 ...
Effective induction of gene regulatory networks using a novel recommendation method
Ozsoy, Makbule Gulcin; Polat, Faruk; Alhajj, Reda (2019-01-01)
In this paper, we introduce a method based on recommendation systems to predict the structure of Gene Regulatory Networks (GRNs) making use of data from multiple sources. Our method is based on collaborative filtering approach enhanced with multiple criteria to predict the relationships of genes, i.e., which genes regulate others. We conduct experiments on two data sets to demonstrate the applicability and sustainability of our proposal. The first data set is composed of microarray data and Transcription Fa...
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.