Two New Nonparametric Models for Biological Networks

Seçilmiş, Deniz; Ağraz, Melih; Purutçuoğlu, Vi̇lda

doi:10.1201/9781003164265-7

Cited by 3 publications

(1 citation statement)

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“…With the increasing amount of data and developments in computer science, the interest in ML has increased in recent years in health sciences. Tree-based algorithms are used in many ML related studies (19,20). In this study, we use tree-based conventional ML algorithms, Decision Trees, Extremely Randomized Trees and Gradient Boosting Trees, since they give the variable importance of the model.…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Comparison of Feature Selection Methods in Breast Cancer Microarray Data

Agraz

2023

Medical Records

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Aim: We aim to predict metastasis in breast cancer patients with conventional machine learning algorithms and to observe which feature selection method is more effective in machine learning methods related to microarray breast cancer data reducing the number of features. Material and Methods: Feature selection methods; least squares absolute shrinkage (LASSO), boruta and maximum relevance-minimum redundancy (MRMR) were first applied before the tree-based learning conventional machine learning methods (Decision-tree, Extremely randomized trees, and Gradient Boosting Tree ) applied on the microarray breast cancer data. Results: Microarray data from 54675 features and 202 (101 metastasis and 101 non-metastasis) patients were reduced to approximately 20 features. It was observed that the highest precision value was obtained from the XGBoost method in the baseline data as 0.9201, but in recall and F1-scores, the highest values were obtained from the MRMR feature selection method as 0.9375 and 0.9090, respectively. The 10 arrays out of 54675 with the highest variable importance were listed. Conclusion: The most accurate results were obtained from the MRMR feature selection method for the XGBoost machine learning algorithm. MRMR method can be used to reduce the dimension of microarray data in breast cancer.

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Section: Machine Learning Algorithmsmentioning

confidence: 99%

Comparison of Feature Selection Methods in Breast Cancer Microarray Data

Agraz

2023

Medical Records

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A novel statistical modeling of air pollution and the COVID-19 pandemic mortality data by Poisson, geometric, and negative binomial regression models with fixed and random effects

İyit,

Sevim

2023

Open Chemistry

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The coronavirus disease 2019 (COVID-19) pandemic was defined by the World Health Organization (WHO) as a global epidemic on March 11, 2020, as the infectious disease that threatens public health fatally. In this study, the main aim is to model the impact of various air pollution causes on mortality data due to the COVID-19 pandemic by Generalized Linear Mixed Model (GLMM) approach to make global statistical inferences about 174 WHO member countries as subjects in the six WHO regions. “Total number of deaths by these countries due to the COVID-19 pandemic” until July 27, 2022, is taken as the response variable. The explanatory variables are taken as the WHO regions, the number of deaths from air pollution causes per 100.000 population as “household air pollution from solid fuels,” “ambient particulate matter pollution,” and “ambient ozone pollution.” In this study, Poisson, geometric, and negative binomial (NB) regression models with “country” taken as fixed and random effects, as special cases of GLMM, are fitted to model the response variable in the aspect of the above-mentioned explanatory variables. In the Poisson, geometric, and NB regression models, Iteratively Reweighted Least Squares parameter estimation method with the Fisher-Scoring iterative algorithm under the log-link function as canonical link function is used. In the GLMM approach, Laplace approximation is also used in the prediction of random effects. In this study, six different Poisson, geometric, and NB regression models with fixed and random effects are established for 174 countries all over the world to make global statistical inferences for investigating the relationships between “total number of deaths” by these countries due to the COVID-19 pandemic and “air pollution causes.” As a result of this study, “NB mixed-effects regression model” as the most appropriate GLMM is used to make global statistical inferences about the impact of the various air pollution causes on the mortality data due to the COVID-19 pandemic.

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Machine learning characterization of a novel panel for metastatic prediction in breast cancer

Agraz¹,

Umut²,

Celeste³

et al. 2022

Glob J Perioperative Med

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Metastasis is one of the most challenging problems in cancer diagnosis and treatment, as causal factors have yet to be fully disentangled. Prediction of the metastatic status of breast cancer is important for informing treatment protocols and reducing mortality. However, the systems biology behind metastasis is complex and driven by a variety of interacting factors. Furthermore, the prediction of cancer metastasis is a challenging task due to the variation in parameters and conditions specific to individual patients and mutation subtypes. In this paper, we apply tree-based machine learning algorithms for gene expression data analysis in the estimation of metastatic potentials within a group of 490 breast cancer patients. Tree-based machine learning algorithms including decision trees, gradient boosting, and extremely randomized trees are used to assess the variable importance of different genes in breast cancer metastasis. ighly accurate values were obtained from all three algorithms, with the gradient boosting method having the highest accuracy at 0.8901. The most significant ten genetic variables and fifteen gene functions in metastatic progression were identified. Respective importance scores and biological functions were also cataloged. Key genes in metastatic breast cancer progression include but are not limited to CD8, PB1, and THP-1.

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Two New Nonparametric Models for Biological Networks

Cited by 3 publications

References 0 publications

Comparison of Feature Selection Methods in Breast Cancer Microarray Data

Comparison of Feature Selection Methods in Breast Cancer Microarray Data

A novel statistical modeling of air pollution and the COVID-19 pandemic mortality data by Poisson, geometric, and negative binomial regression models with fixed and random effects

Machine learning characterization of a novel panel for metastatic prediction in breast cancer

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