Inference of Gene Regulatory Networks Via Multiple Data Sources and a Recommendation Method

Date

2015-11-12

Author

Ozsoy, Makbule Gulcin
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

30
views

0
downloads

Gene regulatory networks (GRNs) are composed of biological components, including genes, proteins and metabolites, and their interactions. In general, computational methods are used to infer the connections among these components. However, computational methods should take into account the general features of the GRNs, which are sparseness, scale-free topology, modularity and structure of the inferred networks. In this work, observing the common aspects between recommendation systems and GRNs, we decided to map the GRNs inspiring problem into a recommendation problem and then used a known recommendation method to predict gene relationships based on multiple data sources, e.g., which molecules regulate others. The method we used is based on Pareto dominance and collaborative filtering. For the experiments, we used a combination of two datasets, namely microarray data and transcription factor (TF) binding data. The reported results show that using information from multiple sources improves the performance. Also, we observed that employing an approach from the recommendation systems domain revealed interesting results and good performance.

Subject Keywords

Recommendation Systems, Collaborative Filtering, Pareto Dominance, Multiple Data Sources, Gene Regulatory Networks

URI

https://hdl.handle.net/11511/55447

Conference Name

IEEE International Conference on Bioinformatics and Biomedicine - Medical Informatics and Decision Making

Collections

Department of Computer Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

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...
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...
Inference of large-scale networks via statistical approaches Ayyıldız Demirci, Ezgi; Purutçuoğlu Gazi, Vilda; Department of Statistics (2019) In system biology, the interactions between components such as genes, proteins, can be represented by a network. To understand the molecular mechanism of complex biological systems, construction of their networks plays a crucial role. However, estimation of these networks is a challenging problem because of their high dimensional and sparse structures. The Gaussian graphical model (GGM) is widely used approach to construct the undirected networks. GGM define the interactions between species by using the con...
Application of copulas in graphical models for inference of biological systems Dokuzoğlu, Damla; Purutçuoğlu Gazi, Vilda; Department of Statistics (2016) Naturally, genes interact with each other by forming a complicated network and the relationship between groups of genes can be showed by different functions as gene networks. Recently, there has been a growing concern in uncovering these complex structures from gene expression data by modeling them mathematically. The Gaussian graphical model (GGM) is one of the very popular parametric approaches for modelling the underlying types of biochemical systems. In this study, we evaluate the performance of this pr...
Functional Constraints on Replacing an Essential Gene with Its Ancient and Modern Homologs Kacar, Betul; Garmendia, Eva; Tunçbağ, Nurcan; Andersson, Dan I.; Hughes, Diarmaid (2017-07-01) Genes encoding proteins that carry out essential informational tasks in the cell, in particular where multiple interaction partners are involved, are less likely to be transferable to a foreign organism. Here, we investigated the constraints on transfer of a gene encoding a highly conserved informational protein, translation elongation factor Tu (EF-Tu), by systematically replacing the endogenous tufA gene in the Escherichia coli genome with its extant and ancestral homologs. The extant homologs represented...

Citation Formats

M. G. Ozsoy, F. Polat, and R. Alhajj, “Inference of Gene Regulatory Networks Via Multiple Data Sources and a Recommendation Method,” presented at the IEEE International Conference on Bioinformatics and Biomedicine - Medical Informatics and Decision Making, Washington, DC, 2015, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/55447.