Ünlü Yazıcı, Miray
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M.Ü. Yazıcı
Miray Unlu
Unlu Yazici, Miray
Unlu, Miray
Yazici, Miray Unlu
Yazıcı, Miray Ünlü
ÜNLÜ, Miray
Ünlü Yazici, Miray
Ünlü, Miray
Miray Unlu
Unlu Yazici, Miray
Unlu, Miray
Yazici, Miray Unlu
Yazıcı, Miray Ünlü
ÜNLÜ, Miray
Ünlü Yazici, Miray
Ünlü, Miray
Job Title
Arş. Gör.
Email Address
miray.unlu@agu.edu.tr
Main Affiliation
04.01. Biyomühendislik
Status
Current Staff
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Scopus Author ID
Turkish CoHE Profile ID
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Sustainable Development Goals
13
CLIMATE ACTION
0
Research Products
17
PARTNERSHIPS FOR THE GOALS
0
Research Products
8
DECENT WORK AND ECONOMIC GROWTH
0
Research Products
9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
0
Research Products
12
RESPONSIBLE CONSUMPTION AND PRODUCTION
0
Research Products
16
PEACE, JUSTICE AND STRONG INSTITUTIONS
0
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11
SUSTAINABLE CITIES AND COMMUNITIES
0
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1
NO POVERTY
0
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6
CLEAN WATER AND SANITATION
2
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10
REDUCED INEQUALITIES
0
Research Products
14
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2
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15
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0
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5
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0
Research Products
4
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0
Research Products
7
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2
Research Products
3
GOOD HEALTH AND WELL-BEING
8
Research Products
2
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0
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Documents
15
Citations
266
h-index
6
Documents
14
Citations
225
Scholarly Output
16
Articles
6
Views / Downloads
1391/397
Supervised MSc Theses
2
Supervised PhD Theses
1
WoS Citation Count
63
Scopus Citation Count
72
WoS h-index
3
Scopus h-index
3
Patents
0
Projects
1
WoS Citations per Publication
3.94
Scopus Citations per Publication
4.50
Open Access Source
7
Supervised Theses
3
| Journal | Count |
|---|---|
| -- 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023 -- Istanbul -- 196805 | 1 |
| -- 27th Signal Processing and Communications Applications Conference, SIU 2019 -- Sivas -- 151073 | 1 |
| 28th Signal Processing and Communications Applications Conference (SIU) -- OCT 05-07, 2020 -- ELECTR NETWORK | 1 |
| -- 4th International Conference on Computer Science and Engineering, UBMK 2019 -- Samsun -- 154916 | 1 |
| Applied Sciences-Basel | 1 |
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16 results
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Now showing 1 - 10 of 16
Article G-S a Prior Biological Knowledge-Based Pattern Detection and Enrichment Framework for Multi-Omics Data Integration(MDPI, 2025) Unlu Yazici, Miray; Bakir-Gungor, Burcu; Yousef, MalikThe rapid advancements in high-throughput technologies have led to a dramatic increase in diverse -omics data types, enabling comprehensive analyses, especially for complex diseases like cancer. Despite the development of multi-omics approaches, the challenges of scaling integration to massive, heterogeneous -omics datasets suggest that novel computational tools need to be designed. In this study, we propose an approach for integrating microRNA (miRNA) and messenger RNA (mRNA) expression data, incorporating prior biological knowledge (PBK). This approach scores and ranks groups of miRNAs and their associated genes using cross-validation iterations. The proposed method incorporates a Pattern detection (P) component to identify molecular motifs unique to each biological group. The analysis also facilitates the visualization of the groups, facilitating the identification of co-occurring groups and their characteristic features across iterations. Furthermore, the groups are scored using an over-representation analysis through a new Enrichment (E) component in each iteration. The clusters of the groups based on the Enrichment Scores (ESs) are visualized in a heatmap to obtain novel insights into the collective behavior and dependencies of the groups, aiming to understand the molecular mechanisms of complex diseases. The developed G-S-M-E tool not only provides performance metrics and biological scores at the group level but also offers comprehensive insights into intricate multi-omics interactions. In summary, our study emphasizes the importance of mathematical and data science methodologies in elucidating intricate multi-omics integration, yielding a formalized approach that deepens our comprehension of complex diseases.Conference Object Citation - Scopus: 1Integrative Analyses in Omics Data: Machine Learning Perspective(Deutsche Gesellschaft fur Medizinische Informatik, Biometrie und Epidemiologie e.V., 2023) Ünlü Yazici, Miray; Bakir-Güngör, Burcu; Yousef, MalikDevelopments in the high throughput technologies have enabled the production of an immense amount of knowledge at the multi-omics level. Considering complex diseases which are affected by multi-factors, single omics datasets might not be sufficient to unveil the molecular mechanisms of heterogeneous diseases. Providing a comprehensive and systematic overview to explain disease hallmarks in significant depth is critical. Utilizing multi-omics datasets has led to the development of a variety of tools and platforms. Machine learning models are utilized in a wide variety of tools to tackle the complexity of disorders and to identify new biomolecular signatures and potential markers. Underlying aspects of these approaches are based on training the models for making predictions and classification of the given data. In this review, we describe current machine learning-based approaches and available implementations. Challenges in the enlightenment of disease mechanisms of onset and progression and future development of the field of medicine will be discussed. The prominence of biological interpretation of model output with corresponding biological knowledge will be also covered in this review. © 2023 Elsevier B.V., All rights reserved.Article Enlightening the Molecular Mechanisms of Type 2 Diabetes With a Novel Pathway Clustering and Pathway Subnetwork Approach(Tubitak Scientific & Technological Research Council Turkey, 2022) Bakir-Gungor, Burcu; Yazici, Miray Unlu; Goy, Gokhan; Temiz, MustafaType 2 diabetes mellitus (T2D) constitutes 90% of the diabetes cases, and it is a complex multifactorial disease. In the last decade, genome-wide association studies (GWASs) for T2D successfully pinpointed the genetic variants (typically single nucleotide polymorphisms, SNPs) that associate with disease risk. In order to diminish the burden of multiple testing in GWAS, researchers attempted to evaluate the collective effects of interesting variants. In this regard, pathway-based analyses of GWAS became popular to discover novel multigenic functional associations. Still, to reveal the unaccounted 85 to 90% of T2D variation, which lies hidden in GWAS datasets, new post-GWAS strategies need to be developed. In this respect, here we reanalyze three metaanalysis data of GWAS in T2D, using the methodology that we have developed to identify disease-associated pathways by combining nominally significant evidence of genetic association with the known biochemical pathways, protein-protein interaction (PPI) networks, and the functional information of selected SNPs. In this research effort, to enlighten the molecular mechanisms underlying T2D development and progress, we integrated different in silico approaches that proceed in top-down manner and bottom-up manner, and presented a comprehensive analysis at protein subnetwork, pathway, and pathway subnetwork levels. Using the mutual information based on the shared genes, the identified protein subnetworks and the affected pathways of each dataset were compared. While most of the identified pathways recapitulate the pathophysiology of T2D, our results show that incorporating SNP functional properties, PPI networks into GWAS can dissect leading molecular pathways, and it could offer improvement over traditional enrichment strategies.Conference Object Citation - WoS: 1Citation - Scopus: 1Kolon Kanserinde Etkilenen Yolak Alt Ağlarini Ve Kümelenmelerini Belirlemek için Yeni Bir Yöntem(Institute of Electrical and Electronics Engineers Inc., 2019) Göy, Gökhan; Ünlü Yazici, Miray; Bakir-Güngör, BurcuNowadays new technological developments that play an important role in the production of big data have brought about the interpretation, sharing and storage of data related to complex diseases. Combining multi-omic data in different molecular levels is potentially important for understanding the biological origin of complex diseases. One of these complex diseases is cancer of different types, which has one of the highest causes of death worldwide. The integration of multiple omic data in the framework of a comprehensive analysis and identification of relevant pathways contribute to the development of therapeutic approaches related to disease. In this study, RNA and methylation data (genes and p values) of colon adenocarcinoma were obtained from TCGA data portal and combined with Fisher's method. While protein subnetworks affected by the disease were identified by using subnetwork algorithm, pathways related to the disease and genes associated with these pathways were determined by functional enrichment analysis. Using gene-pathway relationship matrix, kappa scores of pathways were determined by similarity calculation. In this way, the pathways were clustered according to the hierarchically optimal number, as a result, the most important pathway clusters and related genes that are effective in disease formation identified. © 2020 Elsevier B.V., All rights reserved.Master Thesis QUANTUM DOT BASED BIOSENSING(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2017) ÜNLÜ, MiraySemiconductor nanocrytals also known as quantum dots (QD) with high photoluminesce quantum yield (PLQY), size tunability and favorable optical characteristics occupy a significant area in display technology, solar energy conversion and bioapplications. Size tuning feature of QDs allows emission wavelength ranging from ultraviolet to infrared spectral region. In literature, QD based studies have been performed in visible spectral range by employing mostly cadmium, being a toxic heavy metal. Recently, the search for less toxic alternatives revealed the cadmium free compounds, particularly InP. Cadmium free semiconductor nanocrytals’ potential to be used as fluorescent probes in biodetection and biolabeling area has been proved over the past decades. Pathogens threaten life particularly via water sources like rivers, reservoirs and groundwater. Increasing demand for managing the ‘contamination of drinkable water by pathogenic bacteria’ problem needs a broad perspective about pathogens and their membrane characteristics which are integral part of microorganism detection platforms. Bacteria are categorized mainly upon their membrane properties which are gram negative and gram positive. Extra wall called as peptidoglycan layer in gram positive bacteria makes them more resistant to external forces. Gram negative bacteria with wavy wall is relatively more prone to their environment. One of the most known pathogenic bacteria, E. Coli, have damaged and destroyed many lives throughout the world. High growth rate enables this microorganism to spread around large areas in short time. Therefore, accurate and definite detection of this bacteria in water is crucial. The main frame of this research depends on QD based biodetection of bacteria. First of all, organic based QDs (50% PLQY) containing triocytlyphosphine-sulfur ligand were synthesized and via successful phase transfer, aqueous QDs with 20% PLQY were achieved. Although surface is damaged during ligand exchange procedure, aqueous QDs with high PLQY were obtained. SiO2 was covered with QDs thanks to the attraction between their NH2 group and carboxylic ends, respectively. In the final step, this hybrid structure was covered with SiO2 and silica coated QDs (SCQD) were formed. In order to utilize SCQDs in bacteria detection, fluorescent agents were embeded in polymeric films which were formed by spin coating. As a result, SCQD facilitates the attachment of negatively charged bacteria onto the surface of the films. Appropriately grown DH5 alpha (E. Coli strain) expressing green fluorescent protein (GFP) was used as pathogen in the detection part. SCQD thin films were treated with water containing E.Coli DH5 alpha. Positively charged SCQD attracted negatively charged bacteria and the conjugation between them was analysed with time resolved spectroscopy and monitored with fluorescence microscope. Thus, usage of QDs as biosensor in pathogen detection could provide an insight in the future studies.Conference Object Tip 2 Diyabet'te Etkilenen Yolak Alt Ağlarını Bulmak İçin Yukarıdan Aşağıya İşleyen Bir Yaklaşım(IEEE, 2020) Unlu Yazici, Miray; Bakir-Gungor, BurcuDiabetes Mellitus (DM) is a metabolic disorder caused by dysfunction of insulin-producing pancreatic beta cells, insulin resistance, or impairment of insulin functionality. Type 2 Diabetes Mellitus (T2D) is a complex multifactorial disease that accounts for 90% of diabetes cases. In recent years, genome-wide association studies (GWAS) have successfully identified genetic variants associated with T2D risk. However, while conventional GWAS analyses focus on 'the tip of the iceberg' single nucleotide polymorphisms (SNPs), new analysis methods are needed to uncover hidden variations in these studies. In our previous study, we developed a post-GWAS analysis methodology to find disease-associated marker pathways by integrating human protein-protein interaction network, known biological pathways and potential SNPs. In this study, via adding different in-silico approaches to our methodology, we aim to identify affected pathway subnetworks and affected pathway clusters in addition to the affected protein subnetworks in T2D, and consequently to enlighten molecular mechanisms of T2D. Using this proposed method, we analyzed T2D GWAS meta-analysis data including 12.931 cases ye 57.196 controls. The approach we presented here is based on both the significance value of affected pathway and its topological relationship with other neighbor pathways. In the functional enrichment stage of our method, important pathways were obtained using hypergeometric test and gene-pathway matrix was formed. Then pathway-pathway similarity values were calculated using Jaccard index. Using the scores obtained in the similarity matrix, pathway-pathway network was constructed, and disease-related pathway modules were obtained using subnetwork search algorithms. As a result, genes, pathways and pathway subnetworks that might have a potential role in T2D development were identified, and the categories and classes that are related with these affected pathways were determined.Conference Object Citation - Scopus: 2miRcorrNetPro: Unraveling Algorithmic Insights Through Cross-Validation in Multi-Omics Integration for Comprehensive Data Analysis(Institute of Electrical and Electronics Engineers Inc., 2023) Ünlü Yazici, Miray; Yousef, Malik; Marron, J. S.; Bakir-Güngör, BurcuHigh throughput -omics technologies facilitate the investigation of regulatory mechanisms of complex diseases. Along this line, scientists develop promising tools and methods to extend our understanding at the molecular and functional levels. To this end, miRcorrNet tool performs integrative analysis of MicroRNA (miRNA) and gene expression profiles via machine learning (ML) approach to identify significant miRNA groups and their associated target genes. In this study, we propose miRcorrNetPro tool, which extends miRcorrNet by tracking group scoring, ranking and other information through the cross-validation iterations. Heatmap visualizations enable deep novel insights into the collective behavior of clusters of groups in cellular signaling and hence facilitate detection of potential biomarkers for the disease under investigation. Although miRcorrNetPro is designed as a generic tool, here we present our findings and potential miRNA biomarkers for Breast Cancer (BRCA). The miRcorrNetPro tool and all other supplementary files are available at https://github.com/Miray-Unlu/miRcorrNetPro. © 2024 Elsevier B.V., All rights reserved.Master Thesis Nanokristal Kuantum Nokta Filmler ile Escherichia Coli Arasındaki Etkileşimin İncelenmesi(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Ünlü, Miray; Mutlugün, EvrenSemiconductor nanocrytals also known as quantum dots (QD) with high photoluminesce quantum yield (PLQY), size tunability and favorable optical characteristics occupy a significant area in display technology, solar energy conversion and biotechnology. Size tuning feature of QDs allows peak emission wavelength ranging from ultraviolet to infrared spectral region. In literature, QD based studies have been performed in visible spectral range by employing mostly cadmium, being a toxic heavy metal. Recently, the search for less toxic alternatives revealed the cadmium free compounds, particularly InP. Cadmium free semiconductor nanocrytals' potential to be used as fluorescent probes in biodetection and biolabeling area has been proved over the past decades. Pathogens threaten life particularly via water sources like rivers, reservoirs and groundwater. Increasing demand for managing the 'contamination of drinkable water by pathogenic bacteria' problem needs a broad perspective about pathogens and their membrane characteristics which are integral part of microorganism detection platforms. Bacteria are categorized mainly upon their membrane properties which are gram negative and gram positive. Extra wall called as peptidoglycan layer in gram positive bacteria makes them more resistant to external forces. Gram negative bacteria with wavy wall is relatively more prone to their environment. One of the most known pathogenic bacteria, E. Coli, have damaged and destroyed many lives throughout the world. High growth rate enables this microorganism to spread around large areas in short time. Therefore, accurate and definite detection of this bacteria in water is crucial. The main frame of this research depends on QD based biodetection of bacteria. First of all, organic based QDs (50% PLQY) containing triocytlyphosphine-sulfur ligand were synthesized and via successful phase transfer, QDs in aqueous solvent with 20% PLQY were achieved. Although surface is damaged during ligand exchange procedure, QDs in aqueous solvent with high PLQY were obtained. SiO2 was covered with QDs thanks to the attraction between their NH2 group and carboxylic ends, respectively. In the final step, this hybrid structure was encapsulated with SiO2 and silica coated QDs (SCQD) were formed. In order to utilize SCQDs in bacteria detection, fluorescent agents were embeded in polymeric films which were formed by spin coating. As a result, SCQD facilitates the attachment of negatively charged bacteria onto the surface of the films. Appropriately grown DH5 alpha (E. Coli strain) expressing green fluorescent protein (GFP) was used as pathogen in the detection part. SCQD thin films were treated with water containing E.Coli DH5 alpha. Positively charged SCQD attracted negatively charged bacteria and the conjugation between them was analysed with time resolved spectroscopy and monitored with fluorescence microscope. Thus, usage of QDs as biosensor in pathogen detection could provide an insight in the future studies. Keywords: biodetection, E.coli, quantum dots, semiconductors, silica coated quantum dots, indium phosphate, InP QDArticle 3Mont: A Multi-Omics Integrative Tool for Breast Cancer Subtype Stratification(Public Library Science, 2025) Unlu Yazici, Miray; Marron, J. S.; Bakir-Gungor, Burcu; Zou, Fei; Yousef, MalikBreast Cancer (BRCA) is a heterogeneous disease, and it is one of the most prevalent cancer types among women. Developing effective treatment strategies that address diverse types of BRCA is crucial. Notably, among different BRCA molecular sub-types, Hormone Receptor negative (HR-) BRCA cases, especially Basal-like BRCA sub-types, lack estrogen and progesterone hormone receptors and they exhibit a higher tumor growth rate compared to HR+ cases. Improving survival time and predicting prognosis for distinct molecular profiles is substantial. In this study, we propose a novel approach called 3-Multi-Omics Network and Integration Tool (3Mont), which integrates various -omics data by applying a grouping function, detecting pro-groups, and assigning scores to each pro-group using Feature importance scoring (FIS) component. Following that, machine learning (ML) models are constructed based on the prominent pro-groups, which enable the extraction of promising biomarkers for distinguishing BRCA sub-types. Our tool allows users to analyze the collective behavior of features in each pro-group (biological groups) utilizing ML algorithms. In addition, by constructing the pro-groups and equalizing the feature numbers in each pro-group using the FIS component, this process achieves a significant 20% speedup over the 3Mint tool. Contrary to conventional methods, 3Mont generates networks that illustrate the interplay of the prominent biomarkers of different -omics data. Accordingly, exploring the concerted actions of features in pro-groups facilitates understanding the dynamics of the biomarkers within the generated networks and developing effective strategies for better cancer sub-type stratification. The 3Mont tool, along with all supporting materials, can be found at https://github.com/malikyousef/3Mont.git.Conference Object A New Method to Identify Affected Pathway Subnetworks and Clusters in Colon Cancer(IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA, 2019) Goy, Gokhan; Yazici, Miray Unlu; Bakir-Gungor, BurenNowadays new technological developments that play an important role in the production of big data have brought about the interpretation, sharing and storage of data related to complex diseases. Combining multi-omic data in different molecular levels is potentially important for understanding the biological origin of complex diseases. One of these complex diseases is cancer of different types, which has one of the highest causes of death worldwide. The integration of multiple omic data in the framework of a comprehensive analysis and identification of relevant pathways contribute to the development of therapeutic approaches related to disease. In this study, RNA and methylation data (genes and p values) of colon adenocarcinoma were obtained from TCGA data portal and combined with Fisher's method. While protein subnetworks affected by the disease were identified by using subnetwork algorithm, pathways related to the disease and genes associated with these pathways were determined by functional enrichment analysis. Using gene-pathway relationship matrix, kappa scores of pathways were determined by similarity calculation. In this way, the pathways were clustered according to the hierarchically optimal number, as a result, the most important pathway clusters and related genes that are effective in disease formation identified.
