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

Ayyildiz, Ezgi; Purutçuoğlu, Vi̇lda; Weber, Gerhard

doi:10.1016/j.ejor.2017.12.011

Cited by 15 publications

(5 citation statements)

References 34 publications

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“…The main limitation of this study was the inability of the proposed methodology to cope with large dimensions with more data, and due to this, it is suggested to apply big data analytics methods (Ayyıldız et al, 2018 ; Zdravevski et al, 2020 ) and heuristic/meta-heuristic algorithms (Alinaghian et al, 2020 ) for future research opportunities. Furthermore, other uncertainty techniques such as fuzzy programming (Tirkolaee, Abbasian, et al, 2020; Tirkolaee, Goli, et al, 2020 ), robust scenario-based (Chen et al, 2019 ; Homayouni et al, 2021 ; Lotfi et al, 2021a , 2021b ; Lotfi et al, 2020 ; Özmen et al, 2017 ), optimal control (Savku & Weber, 2018 ) and artificial neural network (Graczyk-Kucharska et al, 2020 ) can be implemented and compared with the proposed RO approach.…”

Section: Discussionmentioning

confidence: 99%

An extended robust mathematical model to project the course of COVID-19 epidemic in Iran

et al. 2022

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This research develops a regression-based Robust Optimization (RO) approach to efficiently predict the number of patients with confirmed infection caused by the recent Coronavirus Disease (COVID-19). The main idea is to study the dynamics of the COVID-19 outbreak at the first stage and then provide efficient insights to estimate the necessary resources accordingly. The convex RO with Mean Absolute Deviation (MAD) objective function is utilized to project the course of COVID-19 epidemic in Iran. To validate the performance of the suggested model, a real-case study is investigated and compared to several well-known forecasting models including Simple Moving Average, Exponential Moving Average, Weighted Moving Average and Exponential Smoothing with Trend Adjustment models. Furthermore, the effect of parameter uncertainties is examined using a set of sensitivity analyses. The results demonstrate that by increasing the degree (coefficient) of regression up to 8, MAD value decreases to 1378.12, and consequently, the corresponding equation becomes more accurate. On the other hand, from the 8th degree onwards, MAD value follows an upward trend. Furthermore, by increasing the level of regression uncertainty, MAD value follows a downward trend to reach 1309.28 and the estimation accuracy of the model increases accordingly. Finally, our proposed model achieves the least MAD and the greatest correlation coefficient against the other models.

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Section: Discussionmentioning

confidence: 99%

An extended robust mathematical model to project the course of COVID-19 epidemic in Iran

et al. 2022

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“…This paper is devoted to the foundations of semialgebraic gene-environment networks, i.e., to bases of the future development of algorithmic methods to assess those networks and to real-work applications. Already today we may mention that the following refined classes of techniques can be naturally suggested for our new class of networks under uncertainty, for their identification, optimization and extension: (i) Tchebychev Approximation [53], (ii) Semi-Infinite Optimization [29], (iii) Generalized Semi-Infinite Optimization [48,49,56], (iv) Bi-level and Multilevel Optimization [36], (v) Disjunctive Optimization [2], (vi) Robust Optimization [24,39,42], (vii) Conic Optimization [4], (viii) Optimal Control [1], and (ix) Stochastic Optimal Control [33]. Concerning classes of future real-world applications we would like to recommend emerging challenges of, for example, (a) Collaborative Games under Ellipsoidal Uncertainty or (per inner or outer approximations) Hypercube Uncertainty [11,52], (b) Transportation ("Piano Mover's" and many more) problems [32,40], (c) Supply Chain and Inventory Management [12,20,30,41,43,57] Production Planning [38], various kinds of (d) Design problems [46], (e) Artificial Intelligence and Machine Learning (e.g., "Infinite Kernel Learning") [6,13,28], and (f ) Finance, Actuarial Sciences and Pension Fund Systems [14,19,55].…”

Section: Definition 52 a Parameter-dependent Target-environment Netmentioning

confidence: 99%

Foundations of semialgebraic gene-environment networks

Kropat¹,

Weber²,

Tirkolaee³

2020

Journal of Dynamics &Amp; Games

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Gene-environment network studies rely on data originating from different disciplines such as chemistry, biology, psychology or social sciences. Sophisticated regulatory models are required for a deeper investigation of the unknown and hidden functional relationships between genetic and environmental factors. At the same time, various kinds of uncertainty can arise and interfere with the system's evolution. The aim of this study is to go beyond traditional stochastic approaches and to propose a novel framework of semialgebraic gene-environment networks. Foundation is laid for future research, methodology and application. This approach is a natural extension of interconnected systems based on stochastic, polyhedral, ellipsoidal or fuzzy (linguistic) uncertainty. It allows for a reconstruction of the underlying network from uncertain (semialgebraic) data sets and for a prediction of the uncertain futures states of the system. In addition, aspects of network pruning for large regulatory systems in genome-wide studies are discussed leading to mixed-integer programming (MIP) and continuous programming.

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“…The backward stage process is applied to prevent overfitting from this complex model, the best model is also obtained by removing some basis functions which indicate a small increase in the residual square error [19][20].…”

Section: Multivariate Adaptive Regression Splinesmentioning

confidence: 99%

The Effectiveness of Different Machine Learning Algorithms on Basketball Players’ Shooting Performance

Depren

2019

Spor Ve Performans Araştırmaları Dergisi

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The main purpose of this study is to determine which factors have an important role in National Basketball Association (NBA) players' shooting accuracy. To achieve this purpose, player-based raw-dataset for each match on the 2014-2015 NBA season is used in this study. Seven different machine learning algorithms are applied and also 10-fold cross-validation with 10-repeat process is performed to avoid the overfitting problem. Nine independent variables and one binary dependent variable are included in the analysis. According to the results of the analysis, k-nearest neighbor algorithm is the best machine learning algorithm among other algorithms that are used in the analysis in order to predict whether basketball player can make a shot or not. Shot Distance, distance of closest defense player and touch time are identified as the most important factors affecting player's successful field goal accuracy. Since the successful field goal performance is very influential in winning the game, the results of this study can be used as a guide for training programs to basketball players and team coaches.

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

Cited by 15 publications

References 34 publications

An extended robust mathematical model to project the course of COVID-19 epidemic in Iran

An extended robust mathematical model to project the course of COVID-19 epidemic in Iran

Foundations of semialgebraic gene-environment networks

The Effectiveness of Different Machine Learning Algorithms on Basketball Players’ Shooting Performance

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