Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Citation - Scopus: 4Quasi-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.Article Identification and Visualization of Cell Subgroups in Uncompensated Flow Cytometry Data(Elsevier Ltd., 2020) Güzel, Başak Esin Köktürk; Karaçalı, BilgeWe propose a new method for identification and visualization of cell-sub groups in uncompensated multi-color flow cytometry data. The method combines annealing-based model-free expectation-maximization to identify cell sub-groups and joint diagonalization on clustered data for better visualization. The proposed method was evaluated on a real, publicly available 8-color flow cytometry dataset manually gated beforehand for lymphocytes. The results obtained in three separable scenarios indicate that the method accurately identifies cell subgroups while properly adjusting visualization of identified cell groups by reducing the spectral overlap between the different fluorochrome channels.Article Citation - Scopus: 1An Efficient Algorithm for Large-Scale Quasi-Supervised Learning(Springer Verlag, 2016) Karaçalı, BilgeWe present a novel formulation for quasi-supervised learning that extends the learning paradigm to large datasets. Quasi-supervised learning computes the posterior probabilities of overlapping datasets at each sample and labels those that are highly specific to their respective datasets. The proposed formulation partitions the data into sample groups to compute the dataset posterior probabilities in a smaller computational complexity. In experiments on synthetic as well as real datasets, the proposed algorithm attained significant reduction in the computation time for similar recognition performances compared to the original algorithm, effectively generalizing the quasi-supervised learning paradigm to applications characterized by very large datasets.