WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Correction Correction: Engineering Novel Features for Diabetes Complication Prediction Using Synthetic Electronic Health Records(Frontiers Media S.A., 2025-08-29) Voskergian, Daniel; Bakir-Gungor, Burcu; Yousef, MalikArticle Citation - WoS: 26Citation - Scopus: 33miRmoduleNet: Detecting miRNA-mRNA Regulatory Modules(Frontiers Media S.A., 2022-04-12) Yousef, Malik; Goy, Gokhan; Bakir-Gungor, BurcuIncreasing evidence that MicroRNAs (miRNAs) play a key role in carcinogenesis has revealed the need for elucidating the mechanisms of miRNA regulation and the roles of miRNAs in gene-regulatory networks. A better understanding of the interactions between miRNAs and their mRNA targets will provide a better understanding of the complex biological processes that occur during carcinogenesis. Increased efforts to reveal these interactions have led to the development of a variety of tools to detect and understand these interactions. We have recently described a machine learning approach miRcorrNet, based on grouping and scoring (ranking) groups of genes, where each group is associated with a miRNA and the group members are genes with expression patterns that are correlated with this specific miRNA. The miRcorrNet tool requires two types of -omics data, miRNA and mRNA expression profiles, as an input file. In this study we describe miRModuleNet, which groups mRNA (genes) that are correlated with each miRNA to form a star shape, which we identify as a miRNA-mRNA regulatory module. A scoring procedure is then applied to each module to further assess their contribution in terms of classification. An important output of miRModuleNet is that it provides a hierarchical list of significant miRNA-mRNA regulatory modules. miRModuleNet was further validated on external datasets for their disease associations, and functional enrichment analysis was also performed. The application of miRModuleNet aids the identification of functional relationships between significant biomarkers and reveals essential pathways involved in cancer pathogenesis.Article Citation - WoS: 20Citation - Scopus: 24miRdisNET: Discovering MicroRNA Biomarkers That Are Associated With Diseases Utilizing Biological Knowledge-Based Machine Learning(Frontiers Media S.A., 2023-01-12) Jabeer, Amhar; Temiz, Mustafa; Bakir-Gungor, Burcu; Yousef, MalikDuring recent years, biological experiments and increasing evidence have shown that MicroRNAs play an important role in the diagnosis and treatment of human complex diseases. Therefore, to diagnose and treat human complex diseases, it is necessary to reveal the associations between a specific disease and related miRNAs. Although current computational models based on machine learning attempt to determine miRNA-disease associations, the accuracy of these models need to be improved, and candidate miRNA-disease relations need to be evaluated from a biological perspective. In this paper, we propose a computational model named miRdisNET to predict potential miRNA-disease associations. Specifically, miRdisNET requires two types of data, i.e., miRNA expression profiles and known disease-miRNA associations as input files. First, we generate subsets of specific diseases by applying the grouping component. These subsets contain miRNA expressions with class labels associated with each specific disease. Then, we assign an importance score to each group by using a machine learning method for classification. Finally, we apply a modeling component and obtain outputs. One of the most important outputs of miRdisNET is the performance of miRNA-disease prediction. Compared with the existing methods, miRdisNET obtained the highest AUC value of .9998. Another output of miRdisNET is a list of significant miRNAs for disease under study. The miRNAs identified by miRdisNET are validated via referring to the gold-standard databases which hold information on experimentally verified MicroRNA-disease associations. miRdisNET has been developed to predict candidate miRNAs for new diseases, where miRNA-disease relation is not yet known. In addition, miRdisNET presents candidate disease-disease associations based on shared miRNA knowledge. The miRdisNET tool and other supplementary files are publicly available at: .Article Citation - WoS: 80Citation - Scopus: 93Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions(Frontiers Media S.A., 2021-02-22) Moreno-Indias, Isabel; Lahti, Leo; Nedyalkova, Miroslava; Elbere, Ilze; Roshchupkin, Gennady; Adilovic, Muhamed; Claesson, Marcus J.The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 "ML4Microbiome" that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.Article Citation - WoS: 1Citation - Scopus: 1PSO Supported Ensemble Algorithm for Bad Data Detection Against Intelligent Hacking Algorithm(Frontiers Media S.A., 2021-07-23) Yavuz, Levent; Soran, Ahmet; Onen, Ahmet; Muyeen, S. M.Power system cybersecurity has recently become important due to cyber-attacks. Due to advanced computer science and machine learning (ML) applications being used by malicious attackers, cybersecurity is becoming crucial to creating sustainable, reliable, efficient, and well-protected cyber-systems. Power system operators are needed to develop sophisticated detection mechanisms. In this study, a novel machine-learning-based detection algorithm that combines the five most popular ML algorithms with Particle Swarm Optimizer (PSO) is developed and tested by using an intelligent hacking algorithm that is specially developed to measure the effectiveness of this study. The hacking algorithm provides three different types of injections: random, continuous random, and slow injections by adaptive manner. This would make detection harder. Results shows that recall values with the proposed algorithm for each different type of attack have been increased.Article Citation - WoS: 9Citation - Scopus: 15MicroBiomeGSM: The Identification of Taxonomic Biomarkers From Metagenomic Data Using Grouping, Scoring and Modeling (G-S-M) Approach(Frontiers Media S.A., 2023-11-22) Bakir-Gungor, Burcu; Temiz, Mustafa; Jabeer, Amhar; Wu, Di; Yousef, MalikNumerous biological environments have been characterized with the advent of metagenomic sequencing using next generation sequencing which lays out the relative abundance values of microbial taxa. Modeling the human microbiome using machine learning models has the potential to identify microbial biomarkers and aid in the diagnosis of a variety of diseases such as inflammatory bowel disease, diabetes, colorectal cancer, and many others. The goal of this study is to develop an effective classification model for the analysis of metagenomic datasets associated with different diseases. In this way, we aim to identify taxonomic biomarkers associated with these diseases and facilitate disease diagnosis. The microBiomeGSM tool presented in this work incorporates the pre-existing taxonomy information into a machine learning approach and challenges to solve the classification problem in metagenomics disease-associated datasets. Based on the G-S-M (Grouping-Scoring-Modeling) approach, species level information is used as features and classified by relating their taxonomic features at different levels, including genus, family, and order. Using four different disease associated metagenomics datasets, the performance of microBiomeGSM is comparatively evaluated with other feature selection methods such as Fast Correlation Based Filter (FCBF), Select K Best (SKB), Extreme Gradient Boosting (XGB), Conditional Mutual Information Maximization (CMIM), Maximum Likelihood and Minimum Redundancy (MRMR) and Information Gain (IG), also with other classifiers such as AdaBoost, Decision Tree, LogitBoost and Random Forest. microBiomeGSM achieved the highest results with an Area under the curve (AUC) value of 0.98% at the order taxonomic level for IBDMD dataset. Another significant output of microBiomeGSM is the list of taxonomic groups that are identified as important for the disease under study and the names of the species within these groups. The association between the detected species and the disease under investigation is confirmed by previous studies in the literature. The microBiomeGSM tool and other supplementary files are publicly available at: https://github.com/malikyousef/microBiomeGSM.Article Citation - WoS: 16Citation - Scopus: 20Invention of 3Mint for Feature Grouping and Scoring in Multi-Omics(Frontiers Media S.A., 2023-03-15) Yazici, Miray Unlu; Marron, J. S.; Bakir-Gungor, Burcu; Zou, Fei; Yousef, Malik; Unlu Yazici, MirayAdvanced genomic and molecular profiling technologies accelerated the enlightenment of the regulatory mechanisms behind cancer development and progression, and the targeted therapies in patients. Along this line, intense studies with immense amounts of biological information have boosted the discovery of molecular biomarkers. Cancer is one of the leading causes of death around the world in recent years. Elucidation of genomic and epigenetic factors in Breast Cancer (BRCA) can provide a roadmap to uncover the disease mechanisms. Accordingly, unraveling the possible systematic connections between-omics data types and their contribution to BRCA tumor progression is crucial. In this study, we have developed a novel machine learning (ML) based integrative approach for multi-omics data analysis. This integrative approach combines information from gene expression (mRNA), MicroRNA (miRNA) and methylation data. Due to the complexity of cancer, this integrated data is expected to improve the prediction, diagnosis and treatment of disease through patterns only available from the 3-way interactions between these 3-omics datasets. In addition, the proposed method bridges the interpretation gap between the disease mechanisms that drive onset and progression. Our fundamental contribution is the 3 Multi-omics integrative tool (3Mint). This tool aims to perform grouping and scoring of groups using biological knowledge. Another major goal is improved gene selection via detection of novel groups of cross-omics biomarkers. Performance of 3Mint is assessed using different metrics. Our computational performance evaluations showed that the 3Mint classifies the BRCA molecular subtypes with lower number of genes when compared to the miRcorrNet tool which uses miRNA and mRNA gene expression profiles in terms of similar performance metrics (95% Accuracy). The incorporation of methylation data in 3Mint yields a much more focused analysis. The 3Mint tool and all other supplementary files are available at .Article Citation - WoS: 16Citation - Scopus: 21GeNetOntology: Identifying Affected Gene Ontology Terms via Grouping, Scoring, and Modeling of Gene Expression Data Utilizing Biological Knowledge-Based Machine Learning(Frontiers Media S.A., 2023-08-21) Ersoz, Nur Sebnem; Bakir-Gungor, Burcu; Yousef, MalikIntroduction: Identifying significant sets of genes that are up/downregulated under specific conditions is vital to understand disease development mechanisms at the molecular level. Along this line, in order to analyze transcriptomic data, several computational feature selection (i.e., gene selection) methods have been proposed. On the other hand, uncovering the core functions of the selected genes provides a deep understanding of diseases. In order to address this problem, biological domain knowledge-based feature selection methods have been proposed. Unlike computational gene selection approaches, these domain knowledge-based methods take the underlying biology into account and integrate knowledge from external biological resources. Gene Ontology (GO) is one such biological resource that provides ontology terms for defining the molecular function, cellular component, and biological process of the gene product.Methods: In this study, we developed a tool named GeNetOntology which performs GO-based feature selection for gene expression data analysis. In the proposed approach, the process of Grouping, Scoring, and Modeling (G-S-M) is used to identify significant GO terms. GO information has been used as the grouping information, which has been embedded into a machine learning (ML) algorithm to select informative ontology terms. The genes annotated with the selected ontology terms have been used in the training part to carry out the classification task of the ML model. The output is an important set of ontologies for the two-class classification task applied to gene expression data for a given phenotype.Results: Our approach has been tested on 11 different gene expression datasets, and the results showed that GeNetOntology successfully identified important disease-related ontology terms to be used in the classification model.Discussion: GeNetOntology will assist geneticists and scientists to identify a range of disease-related genes and ontologies in transcriptomic data analysis, and it will also help doctors design diagnosis platforms and improve patient treatment plans.Article Citation - WoS: 1Citation - Scopus: 3Engineering Novel Features for Diabetes Complication Prediction Using Synthetic Electronic Health Records(Frontiers Media S.A., 2025-04-14) Voskergian, Daniel; Bakir-Gungor, Burcu; Yousef, MalikDiabetes significantly affects millions of people worldwide, leading to substantial morbidity, disability, and mortality rates. Predicting diabetes-related complications from health records is crucial for early prevention and for the development of effective treatment plans. In order to predict four different complications of diabetes mellitus, i.e., retinopathy, chronic kidney disease, ischemic heart disease, and amputations, this study introduces a novel feature engineering approach. While developing the classification models, we utilize XGBoost feature selection method and various supervised machine learning algorithms, including Random Forest, XGBoost, LogitBoost, AdaBoost, and Decision Tree. These models were trained on synthetic electronic health records (EHR) generated by dual-adversarial autoencoders. These EHRs represent nearly 1 million synthetic patients derived from an authentic cohort of 979,308 individuals with diabetes. The variables considered in the models were the age range accompanied by chronic diseases that occur during patient visits starting from the onset of diabetes. Throughout the experiments, XGBoost and Random Forest demonstrated the best overall prediction performance. The final models, which are tailored to each complication and trained using our feature engineering approach, achieved an accuracy between 69% and 77% and an AUC between 77% and 84% using cross-validation, while the partitioned validation approach yielded an accuracy between 59% and 78% and an AUC between 66% and 85%. These findings imply that the performance of our method surpass the performance of the traditional Bag-of-Features approach, highlighting the effectiveness of our approach in enhancing model accuracy and robustness.Article Citation - WoS: 25Citation - Scopus: 31Discovering Potential Taxonomic Biomarkers of Type 2 Diabetes From Human Gut Microbiota via Different Feature Selection Methods(Frontiers Media S.A., 2021-08-25) Bakir-Gungor, Burcu; Bulut, Osman; Jabeer, Amhar; Nalbantoglu, O. Ufuk; Yousef, MalikHuman gut microbiota is a complex community of organisms including trillions of bacteria. While these microorganisms are considered as essential regulators of our immune system, some of them can cause several diseases. In recent years, next-generation sequencing technologies accelerated the discovery of human gut microbiota. In this respect, the use of machine learning techniques became popular to analyze disease-associated metagenomics datasets. Type 2 diabetes (T2D) is a chronic disease and affects millions of people around the world. Since the early diagnosis in T2D is important for effective treatment, there is an utmost need to develop a classification technique that can accelerate T2D diagnosis. In this study, using T2D-associated metagenomics data, we aim to develop a classification model to facilitate T2D diagnosis and to discover T2D-associated biomarkers. The sequencing data of T2D patients and healthy individuals were taken from a metagenome-wide association study and categorized into disease states. The sequencing reads were assigned to taxa, and the identified species are used to train and test our model. To deal with the high dimensionality of features, we applied robust feature selection algorithms such as Conditional Mutual Information Maximization, Maximum Relevance and Minimum Redundancy, Correlation Based Feature Selection, and select K best approach. To test the performance of the classification based on the features that are selected by different methods, we used random forest classifier with 100-fold Monte Carlo cross-validation. In our experiments, we observed that 15 commonly selected features have a considerable effect in terms of minimizing the microbiota used for the diagnosis of T2D and thus reducing the time and cost. When we perform biological validation of these identified species, we found that some of them are known as related to T2D development mechanisms and we identified additional species as potential biomarkers. Additionally, we attempted to find the subgroups of T2D patients using k-means clustering. In summary, this study utilizes several supervised and unsupervised machine learning algorithms to increase the diagnostic accuracy of T2D, investigates potential biomarkers of T2D, and finds out which subset of microbiota is more informative than other taxa by applying state-of-the art feature selection methods.</p>