Browsing by Author "Unlu Yazici, Miray"

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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, Malik
Breast 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.
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, Malik
The 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.
Tip 2 Diyabet'te Etkilenen Yolak Alt Ağlarını Bulmak İçin Yukarıdan Aşağıya İşleyen Bir Yaklaşım
(IEEE, 2020) Unlu Yazici, Miray; Bakir-Gungor, Burcu
Diabetes 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.