PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7645

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  • Article
    Citation - WoS: 1
    Tcgex: a Powerful Visual Interface for Exploring and Analyzing Cancer Gene Expression Data
    (Springernature, 2025) Kus, M. Emre; Sahin, Cagatay; Kilic, Emre; Askin, Arda; Ozgur, M. Mert; Karahanogullari, Gokhan; Ekiz, H. Atakan
    Analyzing gene expression data from the Cancer Genome Atlas (TCGA) and similar repositories often requires advanced coding skills, creating a barrier for many researchers. To address this challenge, we developed The Cancer Genome Explorer (TCGEx), a user-friendly, web-based platform for conducting sophisticated analyses such as survival modeling, gene set enrichment analysis, unsupervised clustering, and linear regression-based machine learning. TCGEx provides access to preprocessed TCGA data and immune checkpoint inhibition studies while allowing integration of user-uploaded data sets. Using TCGEx, we explore molecular subsets of human melanoma and identify microRNAs associated with intratumoral immunity. These findings are validated with independent clinical trial data on immune checkpoint inhibitors for melanoma and other cancers. In addition, we identify cytokine genes that can be used to predict treatment responses to various immune checkpoint inhibitors prior to treatment. Built on the R/Shiny framework, TCGEx offers customizable features to adapt analyses for diverse research contexts and generate publication-ready visualizations. TCGEx is freely available at https://tcgex.iyte.edu.tr, providing an accessible tool to extract insights from cancer transcriptomics data.
  • Article
    Citation - Scopus: 4
    Quasi-Supervised Strategies for Compound-Protein Interaction Prediction
    (John Wiley and Sons Inc, 2022) Çakı, O.; Karaçalı, B.
    In-silico compound-protein interaction prediction addresses prioritization of drug candidates for experimental biochemical validation because the wet-lab experiments are time-consuming, laborious and costly. Most machine learning methods proposed to that end approach this problem with supervised learning strategies in which known interactions are labeled as positive and the rest are labeled as negative. However, treating all unknown interactions as negative instances may lead to inaccuracies in real practice since some of the unknown interactions are bound to be positive interactions waiting to be identified as such. In this study, we propose to address this problem using the Quasi-Supervised Learning (QSL) algorithm. In this framework, potential interactions are predicted by estimating the overlap between a true positive dataset of compound-protein pairs with known interactions and an unknown dataset of all the remaining compound-protein pairs. The potential interactions are then identified as those in the unknown dataset that overlap with the interacting pairs in the true positive dataset in terms of the associated similarity structure. We also address the class-imbalance problem by modifying the conventional cost function of the QSL algorithm. Experimental results on GPCR and Nuclear Receptor datasets show that the proposed method can identify actual interactions from all possible combinations. © 2021 Wiley-VCH GmbH.