Loop-based conic multivariate adaptive regression splines is a novel method for advanced construction of complex biological networks

Date

2018-11-01

Author

Ayyıldız Demirci, Ezgi
Purutçuoğlu Gazi, Vilda
Weber, Gerhard Wilhelm

Metadata

Show full item record

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

Item Usage Stats

172
views

0
downloads

The Gaussian Graphical Model (GGM) and its Bayesian alternative, called, the Gaussian copula graphical model (GCGM) are two widely used approaches to construct the undirected networks of biological systems. They define the interactions between species by using the conditional dependencies of the multivariate normality assumption. However, when the system's dimension is high, the performance of the model becomes computationally demanding, and, particularly, the accuracy of GGM decreases when the observations are far from normality. Here, we suggest a Conic Multivariate Adaptive Regression Splines (CMARS) as an alternative to GGM and GCGM to ameliorate both problems. CMARS is a modified version of the Multivariate Adaptive Regression Spline, a well-known modeling approaches used in Operational Research (OR) to represent biological, environmental, and economic data. The main benefit of this model is its compatibility with high-dimensional and correlated measurements of serious nonlinearity, which allows for a wide field of application. We adapted CMARS to describe biological systems and called it "LCMARS" due to its loop-based description. We then applied LCMARS to simulated and real datasets, and LCMARS produced more accurate results compared to GGM and GCGM. Hereby, the ability to use LCMARS in the description of biological networks has the potential to open up new avenues in the application of OR to computational biology and bioinformatics, and can thus help us better understanding complex diseases like cancer and hepatitis.

Subject Keywords

OR in medicine, Accuracy measures, Biological networks, Gaussian graphical model, Conic multivariate adaptive regression splines

URI

https://hdl.handle.net/11511/43832

Journal

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH

DOI

https://doi.org/10.1016/j.ejor.2017.12.011

Collections

Department of Statistics, Article

Suggestions

OpenMETU
Core

Semi-Bayesian Inference of Time Series Chain Graphical Models in Biological Networks Farnoudkia, Hajar; Purutçuoğlu Gazi, Vilda (null; 2018-09-20) The construction of biological networks via time-course datasets can be performed both deterministic models such as ordinary differential equations and stochastic models such as diffusion approximation. Between these two branches, the former has wider application since more data can be available. In this study, we particularly deal with the probabilistic approaches for the steady-state or deterministic description of the biological systems when the systems are observed though time. Hence, we consider time s...
Bernstein approximations in glasso-based estimation of biological networks Purutçuoğlu Gazi, Vilda; Wit, Ernst (2017-03-01) The Gaussian graphical model (GGM) is one of the common dynamic modelling approaches in the construction of gene networks. In inference of this modelling the interaction between genes can be detected mainly via graphical lasso (glasso) or coordinate descent-based approaches. Although these methods are successful in moderate networks, their performances in accuracy decrease when the system becomes sparser. We here implement a particular type of polynomial transformations, called the Bernstein polynomials, of...
TRACEMIN Fiedler A Parallel Algorithm for Computing the Fiedler Vector Manguoğlu, Murat; Saied, Faisal; Sameh, Ahmed (null; 2010-06-25) The eigenvector corresponding to the second smallest eigenvalue of the Laplacian of a graph, known as the Fiedler vector, has a number of applications in areas that include matrix reordering, graph partitioning, protein analysis, data mining, machine learning, and web search. The computation of the Fiedler vector has been regarded as an expensive process as it involves solving a large eigenvalue problem. We present a novel and efficient parallel algorithm for computing the Fiedler vector of large graphs bas...
Hybrid wavelet-neural network models for time series data Kılıç, Deniz Kenan; Uğur, Ömür; Department of Financial Mathematics (2021-3-3) The thesis aims to combine wavelet theory with nonlinear models, particularly neural networks, to find an appropriate time series model structure. Data like financial time series are nonstationary, noisy, and chaotic. Therefore using wavelet analysis helps better modeling in the sense of both frequency and time. S&P500 (∧GSPC) and NASDAQ (∧ IXIC) data are divided into several components by using multiresolution analysis (MRA). Subsequently, each part is modeled by using a suitable neural network structure. ...
Comparison of two inference approaches in Gaussian graphical models Purutçuoğlu Gazi, Vilda; Wit, Ernst (Walter de Gruyter GmbH, 2017-04-01) Introduction: The Gaussian Graphical Model (GGM) is one of the well-known probabilistic models which is based on the conditional independency of nodes in the biological system. Here, we compare the estimates of the GGM parameters by the graphical lasso (glasso) method and the threshold gradient descent (TGD) algorithm.

Citation Formats

E. Ayyıldız Demirci, V. Purutçuoğlu Gazi, and G. W. Weber, “Loop-based conic multivariate adaptive regression splines is a novel method for advanced construction of complex biological networks,” EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, pp. 852–861, 2018, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/43832.