Extended lasso-type MARS (LMARS) model in the description of biological network

The multivariate adaptive regression splines (MARS) model is one of the well-known, additive non-parametric models that can deal with highly correlated and nonlinear datasets successfully. From our previous analyses, we have seen that lasso-type MARS (LMARS) can be a strong alternative of the Gaussian graphical model (GGM) which is a well-known probabilistic method to describe the steady-state behaviour of the complex biological systems via the lasso regression. In this study, we extend our original LMARS model by taking into account the second-order interaction effects of genes as the representative of the feed-forward loop in biological networks. By this way, we can describe both linear and nonlinear activations of the genes in the same model. We evaluate the performance of our new model under different dimensional simulated and real systems, and then compare the accuracy of the estimates with GGM and LMARS outputs. The results show the advantage of this new model over its close alternatives.


MARS as an alternative approach of Gaussian graphical model for biochemical networks
AYYILDIZ DEMİRCİ, EZGİ; Agraz, Melih; Purutçuoğlu Gazi, Vilda (Informa UK Limited, 2017-01-01)
The Gaussian graphical model (GGM) is one of the well-known modelling approaches to describe biological networks under the steady-state condition via the precision matrix of data. In literature there are different methods to infer model parameters based on GGM. The neighbourhood selection with the lasso regression and the graphical lasso method are the most common techniques among these alternative estimation methods. But they can be computationally demanding when the system's dimension increases. Here, we ...
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İslam, Muhammed Qamarul (Informa UK Limited, 2010-01-01)
In a simple multiple linear regression model, the design variables have traditionally been assumed to be non-stochastic. In numerous real-life situations, however, they are stochastic and non-normal. Estimators of parameters applicable to such situations are developed. It is shown that these estimators are efficient and robust. A real-life example is given.
Diverse classifiers ensemble based on GMDH-type neural network algorithm for binary classification
DAĞ, OSMAN; KAŞIKCI, MERVE; KARABULUT, ERDEM; Alpar, Reha (Informa UK Limited, 2019-12-03)
Group Method of Data Handling (GMDH) - type neural network algorithm is the heuristic self-organizing algorithm to model the sophisticated systems. In this study, we propose a new algorithm assembling different classifiers based on GMDH algorithm for binary classification. A Monte Carlo simulation study is conducted to compare diverse classifier ensemble based on GMDH (dce-GMDH) algorithm to the other well-known classifiers and to give recommendations for applied researchers on the selection of appropriate ...
Representation of Multiplicative Seasonal Vector Autoregressive Moving Average Models
Yozgatlıgil, Ceylan (Informa UK Limited, 2009-11-01)
Time series often contain observations of several variables and multivariate time series models are used to represent the relationship between these variables. There are many studies on vector autoregressive moving average (VARMA) models, but the representation of multiplicative seasonal VARMA models has not been seriously studied. In a multiplicative vector model, such as a seasonal VARMA model, the representation is not unique because of the noncommutative property of matrix multiplication. In this articl...
A cluster tree based model selection approach for logistic regression classifier
Tanju, Ozge; Kalaylıoğlu Akyıldız, Zeynep Işıl (Informa UK Limited, 2018-01-01)
Model selection methods are important to identify the best approximating model. To identify the best meaningful model, purpose of the model should be clearly pre-stated. The focus of this paper is model selection when the modelling purpose is classification. We propose a new model selection approach designed for logistic regression model selection where main modelling purpose is classification. The method is based on the distance between the two clustering trees. We also question and evaluate the performanc...
Citation Formats
M. Agraz and V. Purutçuoğlu Gazi, “Extended lasso-type MARS (LMARS) model in the description of biological network,” JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, pp. 1–14, 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/43577.