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Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/397

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  • Article
    Developing a Label Propagation Approach for Cancer Subtype Classification Problem
    (Tubitak Scientific & Technological Research Council Turkey, 2022-01-01) Guner, Pinar; Bakir-Gungor, Burcu; Coskun, Mustafa
    Cancer is a disease in which abnormal cells grow uncontrollably and invade other tissues. Several types of cancer have various subtypes with different clinical and biological implications. Based on these differences, treatment methods need to be customized. The identification of distinct cancer subtypes is an important problem in bioinformatics, since it can guide future precision medicine applications. In order to design targeted treatments, bioinformatics methods attempt to discover common molecular pathology of different cancer subtypes. Along this line, several computational methods have been proposed to discover cancer subtypes or to stratify cancer into informative subtypes. However, existing works do not consider the sparseness of data (genes having low degrees) and result in an ill-conditioned solution. To address this shortcoming, in this paper, we propose an alternative unsupervised method to stratify cancer patients into subtypes using applied numerical algebra techniques. More specifically, we applied a label propagation based approach to stratify somatic mutation profiles of colon, head and neck, uterine, bladder, and breast tumors. We evaluated the performance of our method by comparing it to the baseline methods. Extensive experiments demonstrate that our approach highly renders tumor classification tasks by largely outperforming the state-of-the-art unsupervised and supervised approaches.
  • Article
    Citation - WoS: 1
    Citation - Scopus: 2
    Protocol for Determining the Average Speed and Frequency of Kinesin and Dynein-Driven Intraflagellar Transport (IFT) in C. Elegans
    (Elsevier, 2022-09) Turan, Merve G.; Kantarci, Hanife; Temtek, Sadiye D.; Cakici, Onur; Cevik, Sebiha; Kaplan, Oktay, I
    Here, we present a protocol to image a fluorescent-labeled intraflagellar trans-port (IFT) component in Caenorhabditis elegans with fluorescence microscopy, including steps of sample preparations, in vivo live-cell imaging, and post -micro-scopy analysis with kymographs. This protocol breaks down all processes into three categories: (1) pre-imaging preparations, (2) preparations for the time of image acquisition, and (3) post-imaging analyses. The protocol can be applied to determine the speed and frequency of moving particles. For complete details on the use and execution of this protocol, please refer to Cevik et al. (2021).
  • 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.