Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article G-S a Prior Biological Knowledge-Based Pattern Detection and Enrichment Framework for Multi-Omics Data Integration(MDPI, 2025-11-29) 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.Article Toward the Design of New Α-Carboline Derivatives Against Anaplastic Lymphoma Kinase (Alk): A Comprehensive in Silico Approach(Wiley-VCH Verlag GmbH, 2025-11) Sari, Ceyhun; Akcok, IsmailAfter the first description of anaplastic lymphoma kinase (ALK) in an anaplastic large cell lymphoma cell line as a nucleophosmin (NPM) fusion partner, ALK and its various fusion partners have been implicated in numerous cancers such as non-small cell lung cancer (NSCLC), anaplastic large cell lymphoma (ALCL), neuroblastoma, and rhabdomyosarcoma. In the last decade, several compounds targeting ALK have been developed and approved by the Food and Drug Administration (FDA). Despite the advances of generations of ALK inhibitors, a recent study highlighted that around half of the ALK-positive NSCLC patients will go through disease progression in response to first-line alectinib, which is a second-generation ALK inhibitor. In this study, we aimed to propose a novel alpha-carboline compound targeting the ALK tyrosine kinase domain to be used against various types of cancer in which ALK fusion proteins may be involved. In this regard, we designed more than 200 alpha-carboline derivatives and investigated their binding properties against ALK tyrosine kinase by using in silico protocols consisting of molecular docking studies, molecular dynamics simulations, MM/PBSA binding free energy calculation, and essential dynamics analysis. Considering the obtained results, we developed two promising candidates, compounds 208 & 209 with -9.05 and -9.80 binding energies, respectively, which demonstrated improved binding profiles over the course of a 300 ns simulation.