Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395

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  • Article
    Citation - WoS: 15
    Citation - Scopus: 15
    PriPath: Identifying Dysregulated Pathways From Differential Gene Expression via Grouping, Scoring, and Modeling With an Embedded Feature Selection Approach
    (BMC, 2023-02-23) Yousef, Malik; Ozdemir, Fatma; Jaber, Amhar; Allmer, Jens; Bakir-Gungor, Burcu
    BackgroundCell homeostasis relies on the concerted actions of genes, and dysregulated genes can lead to diseases. In living organisms, genes or their products do not act alone but within networks. Subsets of these networks can be viewed as modules that provide specific functionality to an organism. The Kyoto encyclopedia of genes and genomes (KEGG) systematically analyzes gene functions, proteins, and molecules and combines them into pathways. Measurements of gene expression (e.g., RNA-seq data) can be mapped to KEGG pathways to determine which modules are affected or dysregulated in the disease. However, genes acting in multiple pathways and other inherent issues complicate such analyses. Many current approaches may only employ gene expression data and need to pay more attention to some of the existing knowledge stored in KEGG pathways for detecting dysregulated pathways. New methods that consider more precompiled information are required for a more holistic association between gene expression and diseases.ResultsPriPath is a novel approach that transfers the generic process of grouping and scoring, followed by modeling to analyze gene expression with KEGG pathways. In PriPath, KEGG pathways are utilized as the grouping function as part of a machine learning algorithm for selecting the most significant KEGG pathways. A machine learning model is trained to differentiate between diseases and controls using those groups. We have tested PriPath on 13 gene expression datasets of various cancers and other diseases. Our proposed approach successfully assigned biologically and clinically relevant KEGG terms to the samples based on the differentially expressed genes. We have comparatively evaluated the performance of PriPath against other tools, which are similar in their merit. For each dataset, we manually confirmed the top results of PriPath in the literature and found that most predictions can be supported by previous experimental research.ConclusionsPriPath can thus aid in determining dysregulated pathways, which applies to medical diagnostics. In the future, we aim to advance this approach so that it can perform patient stratification based on gene expression and identify druggable targets. Thereby, we cover two aspects of precision medicine.
  • Article
    Citation - WoS: 3
    Citation - Scopus: 4
    Investigating the Carbon Border Adjustment Mechanism Transition Process With Linguistic Summarization Method: A Situational Analysis of Exporting Countries
    (Elsevier Sci Ltd, 2024-08) Fidan, Fatma Sener; Aydogan, Sena; Akay, Diyar; Şener Fidan, Fatma
    The Paris Agreement holds significant importance since it establishes a global framework for addressing the issue of climate change and endeavors to mitigate the release of greenhouse gases. The Carbon Border Adjustment Mechanism was introduced as an integral component of this agreement, aiming to oversee the carbon emissions associated with imported items within the European Union and provide compensation for the emissions from the nations engaged in importation. It is essential to analyze the countries involved in exporting to the European Union within the Carbon Border Adjustment Mechanism context to mitigate carbon leakage and effectively support the objectives outlined in the Paris Agreement. This research investigated 104 nations engaged in exporting activities to 27 European Union member countries. The linguistic summarization method, a descriptive data analytics tool, was employed for the analysis. A total of 42 Combined Nomenclature codes were encompassed within the scope of evaluation throughout the transition phase of the Carbon Border Adjustment Mechanism. This study examines the characteristics of exporting nations based on three variables: The Environmental Performance Index, a sustainability indicator; the Region in which the countries are located as classified by the World Bank; and the quantity of Renewable Energy Consumption. Additionally, the study explores the characteristics of EU countries, focusing on their Environmental Performance Index score and geography. The study employed fuzzy sets and the fuzzy c-means algorithm as parts of the linguistic summarization technique. Polyadic quantifiers were used to extract linguistic summaries, resulting in the acquisition of 124,227 summaries. A total of 1594 summaries have a truth degree exceeding 0.9. The findings were effectively utilized to assess the influence of the linguistic summarization approach and offered a valuable viewpoint for decisionmakers needing more expertise in this domain.
  • Conference Object
    Citation - WoS: 2
    Citation - Scopus: 4
    Data 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, Zafer
    Determining 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.
  • Conference Object
    Citation - Scopus: 7
    A Comparative Analysis on Medical Article Classification Using Text Mining & Machine Learning Algorithms
    (Institute of Electrical and Electronics Engineers Inc., 2021-09-15) Kolukisa, Burak; Dedeturk, Bilge Kagan; Dedeturk, Beyhan Adanur; Gulsen, Abdulkadir; Bakal, Gokhan; Guisen, Abdulkadir
    The document classification task is one of the widely studied research fields on multiple domains. The core motivation of the classification task is that the manual classification efforts are impractical due to the exponentially growing document volumes. Thus, we densely need to exploit automated computational approaches, such as machine learning models along with data & text mining techniques. In this study, we concentrated on the classification of medical articles specifically on common cancer types, due to the significance of the field and the decent number of available documents of interest. We deliberately targeted MEDLINE articles about common cancer types because most cancer types share a similar literature composition. Therefore, this situation makes the classification effort relatively more complicated. To this end, we built multiple machine learning models, including both traditional and deep learning architectures. We achieved the best performance (R¿82% F score) by the LSTM model. Overall, our results demonstrate a strong effect of exploiting both text mining and machine learning methods to distinguish medical articles on common cancer types. © 2022 Elsevier B.V., All rights reserved.
  • Conference Object
    Citation - Scopus: 1
    Data-Driven Discovery and DFT Modeling of Fe4H on the Atomistic Level
    (Elsevier B.V., 2024) Zagorac, Dejan; Zagorac, Jelena; Djukic, Milos B.; Bal, Burak; Schön, Johann Christian
    Since their discovery, iron and hydrogen have been two of the most interesting elements in scientific research, with a variety of known and postulated compounds and applications. Of special interest in materials engineering is the stability of such materials, where hydrogen embrittlement has gained particular importance in recent years. Here, we present the results for the Fe-H system. In the past, most of the work on iron hydrides has been focused on hydrogen-rich compounds since they have a variety of interesting properties at extreme conditions (e.g. superconductivity). However, we present the first atomistic study of an iron-rich Fe4H compound which has been predicted using a combination of data mining and quantum mechanical calculations. Novel structures have been discovered in the Fe4H chemical system for possible experimental synthesis at the atomistic level. © 2024 Elsevier B.V., All rights reserved.