Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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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 .Conference Object Tip 2 Diyabet'te Etkilenen Yolak Alt Ağlarını Bulmak İçin Yukarıdan Aşağıya İşleyen Bir Yaklaşım(IEEE, 2020-10-05) 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.