WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7150

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Now showing 1 - 10 of 23
  • Article
    Citation - WoS: 3
    Citation - Scopus: 4
    Quasi-Supervised Strategies for Compound-Protein Interaction Prediction [article]
    (Wiley-VCH Verlag, 2021) Çakı, Onur; Karaçalı, Bilge; Karaçalı, Bilge; 03.05. Department of Electrical and Electronics Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
    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.
  • Article
    Citation - WoS: 10
    Citation - Scopus: 13
    On the Characterization of Cognitive Tasks Using Activity-Specific Short-Lived Synchronization Between Electroencephalography Channels
    (Elsevier, 2021) Olcay, B. Orkan; Olcay, Bilal Orkan; Karaçalı, Bilge; 01.01. Units Affiliated to the Rectorate; 03.05. Department of Electrical and Electronics Engineering; 01. Izmir Institute of Technology; 03. Faculty of Engineering
    Accurate characterization of brain activity during a cognitive task is challenging due to the dynamically changing and the complex nature of the brain. The majority of the proposed approaches assume stationarity in brain activity and disregard the systematic timing organization among brain regions during cognitive tasks. In this study, we propose a novel cognitive activity recognition method that captures the activity-specific timing parameters from training data that elicits maximal average short-lived pairwise synchronization between electroencephalography signals. We evaluated the characterization power of the activity-specific timing parameter triplets in a motor imagery activity recognition framework. The activity-specific timing parameter triplets consist of latency of the maximally synchronized signal segments from activity onset Delta t, the time lag between maximally synchronized signal segments t, and the duration of the maximally synchronized signal segments w. We used cosine-based similarity, wavelet bi-coherence, phase-locking value, phase coherence value, linearized mutual information, and cross-correntropy to calculate the channel synchronizations at the specific timing parameters. Recognition performances as well as statistical analyses on both BCI Competition-III dataset IVa and PhysioNet Motor Movement/Imagery dataset, indicate that the interchannel short-lived synchronization calculated using activity-specific timing parameter triplets elicit significantly distinct synchronization profiles for different motor imagery tasks and can thus reliably be used for cognitive task recognition purposes. (C) 2021 Elsevier Ltd. All rights reserved.
  • Article
    Citation - WoS: 3
    Citation - Scopus: 3
    Automated Labeling of Cancer Textures in Larynx Histopathology Slides Using Quasi-Supervised Learning
    (Science Printers and Publishers Inc., 2014) Önder, Devrim; Karaçalı, Bilge; Sarıoğlu, Sülen; Karaçalı, Bilge; 03.05. Department of Electrical and Electronics Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
    OBJECTIVE: To evaluate the performance of a quasisupervised statistical learning algorithm, operating on datasets having normal and neoplastic tissues, to identify larynx squamous cell carcinomas. Furthermore, cancer texture separability measures against normal tissues are to be developed and compared either for colorectal or larynx tissues. STUDY DESIGN: Light microscopic digital images from histopathological sections were obtained from laryngectomy materials including squamous cell carcinoma and nonneoplastic regions. The texture features were calculated by using co-occurrence matrices and local histograms. The texture features were input to the quasisupervised learning algorithm. RESULTS: Larynx regions containing squamous cell carcinomas were accurately identified, having false and true positive rates up to 21% and 87%, respectively. CONCLUSION: Larynx squamous cell carcinoma versus normal tissue texture separability measures were higher than colorectal adenocarcinoma versus normal textures for the colorectal database. Furthermore, the resultant labeling performances for all larynx datasets are higher than or equal to that of colorectal datasets. The results in larynx datasets, in comparison with the former colorectal study, suggested that quasi-supervised texture classification is to be a helpful method in histopathological image classification and analysis.
  • Article
    Identification and Visualization of Cell Subgroups in Uncompensated Flow Cytometry Data
    (Elsevier Ltd., 2020) Güzel, Başak Esin Köktürk; Karaçalı, Bilge; Karaçalı, Bilge; 03.05. Department of Electrical and Electronics Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
    We 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.
  • Conference Object
    Improved Quasi-Supervised Learning by Expectation-Maximization
    (Institute of Electrical and Electronics Engineers Inc., 2013) Karaçalı, Bilge; Karaçalı, Bilge; 03.05. Department of Electrical and Electronics Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
    In this paper, a new statistical learning method was developed that implements the quasi-supervised learning method in an expectation-maximization loop. First, automatic strategies were generated that separated the samples drawn from different distributions into respective sample sets using the posterior probabilities computed via quasi-supervised learning based on partially separated samples. An expectation-maximization loop was then constructed by combining this procedure with the posterior probability computation step using the new separated sample sets. In controlled experiments on recognition problems with varying difficulties, the proposed method was observed to consistently outperform the plain quasi-supervised learning method.
  • Conference Object
    Quasi-Supervised Learning on Dna Regions in Colon Cancer Histology Slides
    (Institute of Electrical and Electronics Engineers Inc., 2013) Köktürk, Başak Esin; Karaçalı, Bilge; Karaçalı, Bilge; 03.05. Department of Electrical and Electronics Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
    The aim of this study, nuclei base automatic detection of cancerous regions via determination of DNA-rich regions in high definition histology images. In the study; DNA-rich regions were determined using k-means clustering and some mathematical morphology operations, the diseased regions were diagnosed using morphological characteristics via quasi-supervised learning. It's observed that quasi-supervised learning method successfully separates cancerous chromatin regions from others successfully with experiments of colon cross-section histology images.
  • Conference Object
    Citation - WoS: 2
    Türk Makam Müziği Notaları için Otomatik Ezgi Bölütleme
    (Institute of Electrical and Electronics Engineers Inc., 2014) Bozkurt, Barış; Karaçalı, Bilge; Karaçalı, Bilge; Bozkurt, Barış; Karaosmanoğlu, M. Kemal; Ünal, Erdem; 03.05. Department of Electrical and Electronics Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
    Automatic melodic segmentation is one of the important steps in computational analysis of melodic content from symbolic data This widely studied research problem has been very rarely considered for Turkish makam music. In this paper we first present test results for state-of-the-art techniques from literature on Turkish makam music data Then, we present a statistical classification-based segmentation system that exploits the link between makant melodies and usul and makam scale hierarchies together with the well-known features in literature. We show through tests on a large dataset that the proposed system has a higher accuracy.
  • Conference Object
    Citation - Scopus: 2
    Model-Free Expectation Maximization for Divisive Hierarchical Clustering of Multicolor Flow Cytometry Data
    (IEEE, 2014) Köktürk, Başak Esin; Karaçalı, Bilge; Karaçalı, Bilge; 03.05. Department of Electrical and Electronics Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
    This paper proposes a new method for automated clustering of high dimensional datasets. The method is based on a recursive binary division strategy that successively divides an original dataset into distinct clusters. Each binary division is carried out using a model-free expectation maximization scheme that exploits the posterior probability computation capability of the quasi-supervised learning algorithm. The divisions are carried out until a division cost exceeds an adaptively determined limit. Experiment results on synthetic as well as real multi-color flow cytometry datasets showed that the proposed method can accurately capture the prominent clusters without requiring any knowledge on the number of clusters or their distribution models.
  • Conference Object
    Citation - Scopus: 3
    Gri Seviye Birliktelik Matrisi Öznitelikleri ve Manifold Öğrenme Yardımıyla Histoloji Görüntülerinde Otomatik Doku Sınıflandırılması
    (Institute of Electrical and Electronics Engineers Inc., 2009) Önder, Devrim; Karaçalı, Bilge; Karaçalı, Bilge; 03.05. Department of Electrical and Electronics Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
    The aim of this work is to perform automated texture classification of histology slides using grayscale images and manifold learning method. Texture feature vectors were obtained using local gray scale co-occurrence matrices and the dimension of the feature vector space was lowered using Isomap dimension reduction. In a lower dimension feature space, k-means clustering operation was performed in order to provide separate texture clusters. In this work, experimental results were obtained using human kidney histology slides. Corresponding feature vectors and determined texture types were given as results.
  • Article
    Citation - WoS: 14
    Citation - Scopus: 17
    Evaluation of Synchronization Measures for Capturing the Lagged Synchronization Between Eeg Channels: a Cognitive Task Recognition Approach
    (Elsevier, 2019) Olcay, Bilal Orkan; Olcay, Bilal Orkan; Karaçalı, Bilge; Karaçalı, Bilge; 01.01. Units Affiliated to the Rectorate; 03.05. Department of Electrical and Electronics Engineering; 01. Izmir Institute of Technology; 03. Faculty of Engineering
    During cognitive, perceptual and sensory tasks, connectivity profile changes across different regions of the brain. Variations of such connectivity patterns between different cognitive tasks can be evaluated using pairwise synchronization measures applied to electrophysiological signals, such as electroencephalography (EEG). However, connectivity-based task recognition approaches achieving viable recognition performance have been lacking from the literature. By using several synchronization measures, we identify time lags between channel pairs during different cognitive tasks. We employed mutual information, cross correntropy, cross correlation, phase locking value, cosine similarity and nonlinear interdependence measures. In the training phase, for each type of cognitive task, we identify the time lags that maximize the average synchronization between channel pairs. These lags are used to calculate pairwise synchronization values with which we construct the train and test feature vectors for recognition of the cognitive task carried out using Fisher's linear discriminant (FLD) analysis. We tested our framework in a motor imagery activity recognition scenario on PhysioNet Motor Movement/Imagery and BCI Competition-III IVa datasets. For PhysioNet dataset, average performance results ranging between % 51 and % 61 across 20 subjects. For BCI Competition-III dataset, we achieve an average recognition performance of % 76 which is above the minimum reliable communication rate (% 70). We achieved an average accuracy over the minimum reliable communication rate on the BCI Competition-III dataset. Performance levels were lower on the PhysioNet dataset. These results indicate that a viable task recognition system is achievable using pairwise synchronization measures evaluated at the proper task specific lags.