Feature reduction for gene regulatory network control

Tan, Mehmet
Polat, Faruk
Alhajj, Reda
Scalability is one of the most important issues in control problems, including the control of gene regulatory networks. In this paper we argue that it is possible to improve scalability of gene regulatory networks control by reducing the number of genes to be considered by the control policy; and consequently propose a novel method to estimate genes that are less important for control. The reported test results on real and synthetic data demonstrate the applicability and effectiveness of the proposed approach.


Automated Large-Scale Control of Gene Regulatory Networks
Tan, Mehmet; Alhajj, Reda; Polat, Faruk (Institute of Electrical and Electronics Engineers (IEEE), 2010-04-01)
Controlling gene regulatory networks (GRNs) is an important and hard problem. As it is the case in all control problems, the curse of dimensionality is the main issue in real applications. It is possible that hundreds of genes may regulate one biological activity in an organism; this implies a huge state space, even in the case of Boolean models. This is also evident in the literature that shows that only models of small portions of the genome could be used in control applications. In this paper, we empower...
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...
Employing decomposable partially observable Markov decision processes to control gene regulatory networks
Erdogdu, Utku; Polat, Faruk; Alhajj, Reda (2017-11-01)
Objective: Formulate the induction and control of gene regulatory networks (GRNs) from gene expression data using Partially Observable Markov Decision Processes (POMDPs).
Batch Mode TD(lambda) for Controlling Partially Observable Gene Regulatory Networks
Sirin, Utku; Polat, Faruk; Alhajj, Reda (2017-11-01)
External control of gene regulatory networks (GRNs) has received much attention in recent years. The aim is to find a series of actions to apply to a gene regulation system making it avoid its diseased states. In this work, we propose a novel method for controlling partially observable GRNs combining batch mode reinforcement learning (Batch RL) and TD(lambda) algorithms. Unlike the existing studies inferring a computational model from gene expression data, and obtaining a control policy over the constructed...
Citation Formats
M. Tan, F. Polat, and R. Alhajj, “Feature reduction for gene regulatory network control,” 2007, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/41591.