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Yayın Çizge evrişim ağı kullanarak patojen-konak ağlarında protein etkileşim tahmini(IEEE, 2021-06-09) Koca, Mehmet Burak; Karadeniz, İlknur; Nourani, Esmaeil; Sevilgen, Fatih ErdoğanProteinler yaşamsal faaliyetlerin gerçekleşmesinde kritik rol oynayan biyolojik moleküllerdir. Konak canlı proteinleri ile patojen proteinleri arasındaki etkileşimler patojenkonak etkileşim (PHI) ağlarını oluşturmaktadır. Bu iki parçalı etkileşim ağları patojenin hangi yaşamsal faaliyetleri etkilediğini belirlemede ve dolayısıyla sebep olabileceği hastalıkların tespitinde büyük öneme sahiptir. Proteinler arası etkileşimlerin laboratuvar ortamında tespiti hem zaman alıcı hem de maliyetlidir. Deneysel olarak saptanabilen etkileşim sayısının kısıtlı olması ve bazı etkileşimlerin gözden kaçması hesaplamalı tahmin yöntemlerinin geliştirilmesine önayak olmaktadır. Bu çalışmada PHI ağlarında protein etkileşim tahmini yapmayı sağlayan çizge evrişim ağı (GCN) tabanlı bir yöntem sunulmaktadır. Gözetimsiz olarak eğitilen GCN modeli (GraphSAGE) topolojik bilginin yanı sıra temel öznitelik olarak amino asit dizilimlerini kullanmaktadır. Bu çalışma bildiğimiz kadarıyla PHI ağlarında GCN tabanlı etkileşim tahmini sağlayan ilk çalışmadır. Deneysel sonuçlar geliştirilen modelin kıyaslama için kullanılan PHI veri seti üzerinde yüksek performanslı algoritmalardan %10 daha iyi performans göstererek %96 oranında doğrulukla etkileşim tahmini yaptığını göstermektedir.Yayın ISIKUN at the FinCausal 2020: Linguistically informed machine-learning approach for causality identification in financial documents(Association for Computational Linguistics (ACL), 2020) Özenir, Hüseyin Gökberk; Karadeniz, İlknurThis paper presents our participation to the FinCausal-2020 Shared Task whose ultimate aim is to extract cause-effect relations from a given financial text. Our participation includes two systems for the two sub-tasks of the FinCausal-2020 Shared Task. The first sub-task (Task-1) consists of the binary classification of the given sentences as causal meaningful (1) or causal meaningless (0). Our approach for the Task-1 includes applying linear support vector machines after transforming the input sentences into vector representations using term frequency-inverse document frequency scheme with 3-grams. The second sub-task (Task-2) consists of the identification of the cause-effect relations in the sentences, which are detected as causal meaningful. Our approach for the Task-2 is a CRF-based model which uses linguistically informed features. For the Task-1, the obtained results show that there is a small difference between the proposed approach based on linear support vector machines (F-score 94%), which requires less time compared to the BERT-based baseline (F-score 95%). For the Task-2, although a minor modifications such as the learning algorithm type and the feature representations are made in the conditional random fields based baseline (F-score 52%), we have obtained better results (F-score 60%). The source codes for the both tasks are available online (https://github.com/ozenirgokberk/FinCausal2020.git/).Yayın Graph convolutional network based virus-human protein-protein interaction prediction for novel viruses(Elsevier Ltd, 2022-08-13) Koca, Mehmet Burak; Nourani, Esmaeil; Abbasoğlu, Ferda; Karadeniz, İlknur; Sevilgen, Fatih ErdoğanComputational identification of human-virus protein-protein interactions (PHIs) is a worthwhile step towards understanding infection mechanisms. Analysis of the PHI networks is important for the determination of path-ogenic diseases. Prediction of these interactions is a popular problem since experimental detection of PHIs is both time-consuming and expensive. The available methods use biological features like amino acid sequences, molecular structure, or biological activities for prediction. Recent studies show that the topological properties of proteins in protein-protein interaction (PPI) networks increase the performance of the predictions. The basic network projections, random-walk-based models, or graph neural networks are used for generating topologically enriched (hybrid) protein embeddings. In this study, we propose a three-stage machine learning pipeline that generates and uses hybrid embeddings for PHI prediction. In the first stage, numerical features are extracted from the amino acid sequences using the Doc2Vec and Byte Pair Encoding method. The amino acid embeddings are used as node features while training a modified GraphSAGE model, which is an improved version of the graph convolutional network. Lastly, the hybrid protein embeddings are used for training a binary interaction classifier model that predicts whether there is an interaction between the given two proteins or not. The proposed method is evaluated with comprehensive experiments to test its functionality and compare it with the state-of-art methods. The experimental results on the benchmark dataset prove the efficiency of the proposed model by having a 3–23% better area under curve (AUC) score than its competitors.Yayın ISIKSumm at BioLaySumm task 1: BART-based summarization system enhanced with Bio-entity labels(Association for Computational Linguistics (ACL), 2023-07-13) Çolak, Çağla; Karadeniz, İlknurCommunicating scientific research to the general public is an essential yet challenging task. Lay summaries, which provide a simplified version of research findings, can bridge the gap between scientific knowledge and public understanding. The BioLaySumm task (Goldsack et al., 2023) is a shared task that seeks to automate this process by generating lay summaries from biomedical articles. Two different datasets that have been created from curating two biomedical journals (PLOS and eLife) are provided by the task organizers. As a participant in this shared task, we developed a system to generate a lay summary from an article’s abstract and main text.Yayın BOUN-ISIK participation: an unsupervised approach for the named entity normalization and relation extraction of Bacteria Biotopes(Association for Computational Linguistics (ACL), 2019-11-04) Karadeniz, İlknur; Tuna, Ömer Faruk; Özgu, ArzucanThis paper presents our participation at the Bacteria Biotope Task of the BioNLP Shared Task 2019. Our participation includes two systems for the two subtasks of the Bacteria Biotope Task: the normalization of entities (BB-norm) and the identification of the relations between the entities given a biomedical text (BB-rel). For the normalization of entities, we utilized word embeddings and syntactic re-ranking. For the relation extraction task, pre-defined rules are used. Although both approaches are unsupervised, in the sense that they do not need any labeled data, they achieved promising results. Especially, for the BB-norm task, the results have shown that the proposed method performs as good as deep learning based methods, which require labeled data.
