WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
Browse
21 results
Search Results
Conference Object Enhancing Complex Disease Group Scoring with Mirgedinet: A Multi-Algorithm Machine Learning Framework Based on the GSM Approach(IEEE, 2025-06-25) Qumsiyeh, Emma; Bakir-Gungor, Burcu; Yousef, MalikIntegrating biological prior knowledge for disease gene associations has shown significant promise in discovering new biomarkers with potential translational applications. This work investigates the application of a multi-algorithm machine learning framework based on the Grouping-Scoring-Modeling (G-S-M) approach for improving the prediction of complex diseases. The study identifies the primary gene and miRNA interactions in various complex diseases with the help of miRGediNET, which is a machine-learning based tool that integrates data from three biological databases. Traditional methods have only focused on independence between features; the G-S-M method focuses on aggregating genes based on biological interactions, pinpointing the scoring of gene groups for a disease, and modeling its predictive capability using advanced machine learning algorithms. In this research paper, seven algorithms, including Support Vector Machine, Decision Tree, and CatBoost, were applied to eight datasets extracted from the GEO database. This framework proved very robust in ranking gene clusters, thus predicting critical biomarkers while doing 100-fold randomized cross-validation within the evaluation. The results indicate this approach's high potential for refining disease and supporting research for choosing the best algorithm that can provide biological insights and computational advances.Conference Object Exploring Microbiome Signatures in Autism Spectrum Disorder via Grouping-Scoring Based Machine Learning(IEEE, 2025-06-25) Temiz, Mustafa; Ersoz, Nur Sebnem; Yousef, Malik; Bakir-Gungor, BurcuThe rapid increase in omic data production increased the importance of machine learning (ML) methods to analze these data. In particular, the use of metagenomic data in the diagnosis, prognosis and treatment of diseases is becoming widespread. Autism Spectrum Disorder (ASD) is a neurodevelopmental disease that occurs in early childhood and continues lifelong. The aim of this study is to increase ML performance, reduce computational costs and achieve successful classification performance using a small number of metagenomic features. In addition, disease prediction is performed; ASD associated biomarkers are determined using the microBiomeGSM on metagenomic data. Classification is performed at three different taxonomic levels (genus, family and order) using the relative abundance values of species. The best performance metric (0.95 AUC) was obtained at the order taxonomic level using an average of 416 features with microBiomeGSM. The identified ASD-related taxonomic species are presented.Conference Object Citation - WoS: 1Citation - Scopus: 1Prediction of Type 2 Diabetes Using Metagenomic Data and Identification of Taxonomic Biomarkers(IEEE, 2024-05-15) Temiz, Mustafa; Kuzudisli, Cihan; Yousef, Malik; Bakir-Gungor, BurcuNowadays, different molecular levels of -omics data on diseases are generated and analyzing these data with machine learning methods is one of the popular research topics. Among these data, the use of metagenomic data to facilitate the diagnosis, detection and treatment of diseases is increasing day by day. Type 2 diabetes (T2D) is a chronic disease characterized by insulin resistance and progressive dysfunction of pancreatic beta cells. While the number of people with diabetes is increasing by around 8% annually, the cost of treating the disease is rising by 18% per year. Therefore, the number of studies on the diagnosis, development and progression of T2D is increasing over time. The aim of this study is to achieve higher machine learning performance by using fewer metagenomic features and to achieve better classification performance by reducing computational costs. In this study, we compare the performance of three different methods using T2D-related metagenomic data. First, the MetaPhlAn tool is used to calculate the taxonomic species and their relative abundances in each sample. The SVM-RCE, RCE-IFE and microBiomeGSM tools used in this study are methods that perform classification by grouping and scoring features and are known to work well on complex datasets. In this study, the best results were obtained with the RCE-IFE tool with an AUC of 0.72 with an average of 125 features information. In addition, key taxonomic species identified by these tools as associated with T2D are presented in comparison to the literature.Conference Object Citation - Scopus: 1NLP-Driven Fake News Detection: A Machine Learning Perspective(IEEE, 2025-05-23) Coban, Mert Korkut; Bakal, GokhanThe rapid spread of fake news poses a significant challenge, impacting public opinion, decision-making, and societal trust. This study explores the application of Natural Language Processing (NLP) and Machine Learning (ML) techniques for robust fake news detection. Using datasets such as ISOT Fake News, WELFake, and Football Fake News, the project employs advanced preprocessing methods and feature extraction techniques, including TF-IDF, Word2Vec, and GloVe. A comprehensive evaluation of machine learning models-Random Forest, Support Vector Machines (SVM), and Neural Networks-was conducted to identify the optimal configuration. Results demonstrate that Random Forest with TF-IDF excels in in-domain detection, achieving an F1-score of 99.70%, while Neural Networks paired with Word2Vec and GloVe embeddings outperform in cross-dataset scenarios. The study highlights the importance of dataset size, domain relevance, and feature representation in achieving high generalizability. These findings provide a scalable framework for combating misinformation on digital platforms.Conference Object Metagenomic Data Analysis With Machine Learning to Discover Colorectal Cancer-Associated Enzymes(IEEE, 2024-05-15) Ersoz, Nur Sebnem; Kuzudisli, Cihan; Yousef, Malik; Bakir-Gungor, BurcuThe human gut microbiome comprises over 10 trillion microbes and plays important roles in maintaining metabolism, body homeostasis, impacting immune function. Metagenomics which studies genomic data from clinical and environmental samples is crucial in understanding the interplay between the host and the gut microbiome. Recently, functional profiling of metagenomes helps to identify alterations in microbial functions, particularly enzyme-encoding genes. Colorectal cancer (CRC) is known as one of the leading causes of cancer-related deaths. In this study, we aimed to find the CRC-associated enzymes by analyzing metagenomic data with different machine learning methods. A total of 1262 samples including CRC and control groups from different countries were used in this study. This dataset was obtained by functionally profiling metagenomics data and estimating community level enzyme commission (EC) abundance values. For the analysis of this dataset, RCE-IFE and SVM-RCE machine learning methods, which are group-based feature selection methods, were compared with 6 different individual feature selection methods. 10 times Monte-Carlo Cross Validation was used in our experiments. It was observed that RCE-IFE, Extreme Gradient Boosting and Select K Best methods similarly provided the best performances. Especially in this study, besides the its high performance, the group-based feature selection method RCE-IFE grouped enzymes into clusters unlike TFS, and then identified biologically relevant CRC-associated enzymes.Article Citation - WoS: 2Machine Learning Based Network Intrusion Detection With Hybrid Frequent Item Set Mining(Gazi Univ, 2024-10-02) Firat, Murat; Bakal, Gokhan; Akbas, Ayhan; Bakal, MehmetWith the development and expansion of computer networks day by day and the diversity of software developed, the damage that possible attacks can cause is increasing beyond the predictions. Intrusion Detection Systems (STS/IDS) are one of the practical defense tools against these potential attacks that are constantly growing and diversifying. Thus, one of the emerging methods among researchers is to train these systems with various artificial intelligence methods to detect subsequent attacks in real time and take the necessary precautions. However, the ultimate goal is to propose a hybrid feature selection approach to improve the classification performance. The raw dataset originally enclosed 85 descriptor features (attributes) for classification. These attributes are extracted using CICFlowMeter from a PCAP file where network traffic is recorded for data curation. In this study, classical feature selection methods and frequent item set mining approaches were employed in feature selection for constructing a hybrid model. We aimed to examine the effect of the proposed hybrid feature selection approach on the classification task for the network traffic data containing ordinary and attack records. The outcomes demonstrate that the proposed method gained nearly 3% improvement when applied with the Logistic Regression algorithm on classifying more than 225,000 records.Conference Object Citation - WoS: 16Citation - Scopus: 20Machine Learning Analysis of Inflammatory Bowel Disease-Associated Metagenomics Dataset(Institute of Electrical and Electronics Engineers Inc., 2018-09) Hacilar, Hilal; Nalbantoĝlu, Özkan Ufuk; Bakir-Güngör, BurcuThere is an ongoing interplay between humans and our microbial communities. The microorganisms living in our gut produce energy from our food, strengthen our immune system, break down foreign products, and release metabolites and hormones, which are significant for regulating our physiology. The shifts away from this 'healthy' gut microbiome is considered to be associated with many diseases. Inflammatory bowel diseases (IBD) including Crohn's disease and ulcerative colitis, are gut related disorders affecting the intestinal tract. Although some metagenomics studies are conducted on IBD recently, our current understanding of the precise relationships between the human gut microbiome and IBD remains limited. In this regard, the use of state-of-the art machine learning approaches became popular to address a variety of questions like early diagnosis of certain diseases using human microbiota. In this study, we investigate which subset of gut microbiota are mostly associated with IBD and if disease-associated biomarkers can be detected via applying state-of-the art machine learning algorithms and proper feature selection methods. © 2019 Elsevier B.V., All rights reserved.Conference Object Citation - WoS: 3Citation - Scopus: 3Examining Tongue Movement Intentions in EEG With Machine and Deep Learning: An Approach for Dysphagia Rehabilitation(IEEE, 2024-08-26) Aslan, Sevgi Gokce; Yilmaz, BulentDysphagia, a common swallowing disorder particularly prevalent among older adults and often associated with neurological conditions, significantly affects individuals' quality of life by negatively impacting their eating habits, physical health, and social interactions. This study investigates the potential of brain-computer interface (BCI) technologies in dysphagia rehabilitation, focusing specifically on motor imagery paradigms based on EEG signals and integration with machine learning and deep learning methods for tongue movement. Traditional machine learning classifiers, such as K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, Naive Bayes, Random Forest, AdaBoost, Bagging, Kernel, and Neural Network were employed in discrimination of rest and imagination phases of EEG signals obtained from 30 healthy subjects. Scalogram images obtained using continuous wavelet transform of EEG signals corresponding to the rest and imagination phases of the experiment were used as the input images to the CNN architecture. As a result, KNN and SVM, exhibited lower accuracy rates compared to ensemble methods like AdaBoost and Random Forest, which are effective in handling complex datasets. Additionally, a deep learning approach achieved an accuracy rate of 83%. Overall, this study demonstrates the promising role of BCI technologies and machine learning techniques in dysphagia rehabilitation.Conference Object Data-Driven Local Control Design for Dead Band Control of Load Tap Changers(IEEE, 2024-09-02) Savasci, Alper; Ceylan, Oguzhan; Paudyal, SumitThis study presents an off-line optimization-guided machine learning approach for coordinating the local control rules of on-load tap changers (OLTCs) and step-voltage regulators (SVRs). Based on a bang-bang control rule, these legacy devices autonomously regulate the feeder voltage around the nominal level by varying the tap position in the lower or raise direction. The characterizing parameter of the local control rule is the dead band, which affects the number of tap switching in operation and is directly related to the economical use life of the equipment. The bandwidth is typically set within a standard voltage range and is generally kept constant in daily operation. However, adjusting the bandwidth dynamically can prevent excessive tap switching while maintaining satisfactory voltage regulation for varying loading and distributed generation conditions. Our approach aims to set the bandwidth parameter systematically and efficiently through a machine learning-based scheme, which is trained with a dataset formed by solving the distribution network optimal power flow (DOPF) problem. The performance of learning the bandwidth parameter is demonstrated on the modified 33-node feeder, which is promising for integrated voltage control schemes.Conference Object Citation - WoS: 2Citation - Scopus: 4Data Mining Techniques in Direct Marketing on Imbalanced Data Using Tomek Link Combined With Random Under-Sampling(Assoc Computing Machinery, 2021-05-27) Yilmaz, Umit; Gezer, Cengiz; Aydin, Zafer; Gungor, V. CaGri; Yllmaz, Ümit; Aydln, ZaferDetermining the potential customers is very important in direct marketing. Data mining techniques are one of the most important methods for companies to determine potential customers. However, since the number of potential customers is very low compared to the number of non-potential customers, there is a class imbalance problem that significantly affects the performance of data mining techniques. In this paper, different combinations of basic and advanced resampling techniques such as Synthetic Minority Over-sampling Technique (SMOTE), Tomek Link, RUS, and ROS were evaluated to improve the performance of customer classification. Different feature selection techniques are used in order the decrease the number of non-informative features from the data such as Information Gain, Gain Ratio, Chi-squared, and Relief. Classification performance was compared and utilized using several data mining techniques, such as LightGBM, XGBoost, Gradient Boost, Random Forest, AdaBoost, ANN, Logistic Regression, Decision Trees, SVC, Bagging Classifier based on ROC AUC and sensitivity metrics. A combination of Tomek Link and Random Under-Sampling as a resampling technique and Chi-squared method as feature selection algorithm showed superior performance among the other combinations. Detailed performance evaluations demonstrated that with the proposed approach, LightGBM, which is a gradient boosting algorithm based on decision tree, gave the best results among the other classifiers with 0.947 sensitivity and 0.896 ROC AUC value.
- «
- 1 (current)
- 2
- 3
- »