Inference of large-scale networks via statistical approaches

Download

index.pdf

Date

2019

Author

Ayyıldız Demirci, Ezgi

Metadata

Show full item record

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

Item Usage Stats

316
views

113
downloads

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 conditional dependencies of the multivariate normality assumption. However, when the dimension of the systems is high, the performance of the model becomes computationally demanding, and the accuracy of GGM decreases when the observations are far from normality. In this thesis, we suggest a conic multivariate adaptive regression splines (CMARS) as an alternative to GGM to overcome both problems. CMARS is one of the recent nonparametric methods developed for high dimensional and correlated data. We adapted CMARS to describe biological systems and called it “LCMARS” due to its loop-based description. Here, we generate various scenarios based on distinct distributions and dimensions to compare the performance of LCMARS with MARS and GGM in terms of accuracy measures via Monte Carlo runs. Additionally, different real biological datasets are used to observe the performance of underlying methods. Furthermore, in this study, we perform various outlier detection methods as a pre-processing step before modeling the networks in order to investigate whether the outlier detection can improve the accuracy of the model. In the analysis, several synthetic and real benchmark biological datasets are used.

Subject Keywords

Mathematical statistics., Gaussian graphical model, Conic multivariate adaptive regression splines, Protein-protein interaction networks, Multivariate adaptive regression splines, Outlier detection.

URI

http://etd.lib.metu.edu.tr/upload/12623906/index.pdf
https://hdl.handle.net/11511/45469

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

Inference of Gene Regulatory Networks Via Multiple Data Sources and a Recommendation Method Ozsoy, Makbule Gulcin; Polat, Faruk; Alhajj, Reda (2015-11-12) 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 ...
Modeling of various biological networks via LCMARS AYYILDIZ DEMİRCİ, EZGİ; Purutçuoğlu Gazi, Vilda (Elsevier BV, 2018-09-01) 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 biological networks is a challenging problem because of their high dimensional and sparse structures. Several statistical methods are proposed to overcome this issue. The Conic Multivariate Adaptive Regression Splines (CMARS) is one of the recent n...
PageRank-flux On Graphlet-Guided-Network(PRO-GGNet): A Method for Pathway Reconstruction and Multi-Omic Data Integration Arıcı, Kaan; Tunçbağ, Nurcan (Orta Doğu Teknik Üniversitesi Enformatik Enstitüsü; 2022-10) The recent advancement of omic technologies provides snapshots of cells, tissues, or patients identifying prominent genes, proteins, metabolites, and small molecules. However, accumulated big data on various omic data types may inherently make diseases or perturbations incomprehensible. Network inference or reconstruction methods map a set of significantly altered proteins/genes/metabolites to a given reference network that is composed of already known relations or interactions. Followingly, the signals fro...
Abiotic stress tolerance and growth responses of transgenic potato (Solanum tuberosum L. cv. Kennebec) plants expressing rice Osmyb4 gene AYDIN, GÜLSÜM; Yucel, Meral; Öktem, Hüseyin Avni (2012-09-23) MYB transcription factors are involved in diverse biochemical and physiological processes such as regulation of secondary metabolism, meristem formation, cell morphogenesis and floral and seed development. They are also involved in certain defence and stress responses and in hormone signalling. In the present study, we developed transgenic potato (Solanum tuberosum L. cv. Kennebec) expressing Oryza sativa myb4 gene, encoding MYB4 transcription factor, driven by either CaMV35S constitutive promoter or cold i...
Comparative phosphoproteomic analysis reveals signaling networks regulating monopolar and bipolar cytokinesis. KARAYEL, Ö; ŞANAL, E; GIESE, SH; Üretmen, Kagıalı; POLAT, AN; HU, CK; RENARD, BY; Tunçbağ, Nurcan; ÖZLÜ, N (2018-02-02) The successful completion of cytokinesis requires the coordinated activities of diverse cellular components including membranes, cytoskeletal elements and chromosomes that together form partly redundant pathways, depending on the cell type. The biochemical analysis of this process is challenging due to its dynamic and rapid nature. Here, we systematically compared monopolar and bipolar cytokinesis and demonstrated that monopolar cytokinesis is a good surrogate for cytokinesis and it is a well-suited system ...

Citation Formats

E. Ayyıldız Demirci, “Inference of large-scale networks via statistical approaches,” Thesis (Ph.D.) -- Graduate School of Natural and Applied Sciences. Statistics., Middle East Technical University, 2019.