1.8k citations · 2.2k across the 17 of their papers we have counts for
19 papers
Hate Speech Detection in Turkish and Arabic: A Comprehensive Study
Somaiyeh Dehghan, Gökçe Uludoğan, Mehmet Umut Şen +3
Online hate speech has been linked to a global rise in violence against minorities, including incidents such as mass shootings, lynchings, and ethnic cleansing. Societies grappling…
Linguistic Laws Meet Protein Sequences: A Comparative Analysis of Subword Tokenization Methods
Burak Suyunu, Enes Taylan, Arzucan Özgür
Tokenization is a crucial step in processing protein sequences for machine learning models, as proteins are complex sequences of amino acids that require meaningful segmentation to…
Generative Language Models on Nucleotide Sequences of Human Genes
Musa Nuri Ihtiyar, Arzucan Ozgur
Language models, especially transformer-based ones, have achieved colossal success in NLP. To be precise, studies like BERT for NLU and works like GPT-3 for NLG are very important.…
Evaluation of GPT and BERT-based models on identifying protein-protein interactions in biomedical text
Hasin Rehana, Nur Bengisu Çam, Mert Basmaci +5
Detecting protein-protein interactions (PPIs) is crucial for understanding genetic mechanisms, disease pathogenesis, and drug design. However, with the fast-paced growth of biomedi…
Exploring Data-Driven Chemical SMILES Tokenization Approaches to Identify Key Protein-Ligand Binding Moieties
Asu Büşra Temizer, Gökçe Uludoğan, Rıza Özçelik +5
Machine learning models have found numerous successful applications in computational drug discovery. A large body of these models represents molecules as sequences since molecular…
Exploiting Pretrained Biochemical Language Models for Targeted Drug Design
Gökçe Uludoğan, Elif Ozkirimli, Kutlu O. Ulgen +2
Motivation: The development of novel compounds targeting proteins of interest is one of the most important tasks in the pharmaceutical industry. Deep generative models have been ap…
Enhancements to the BOUN Treebank Reflecting the Agglutinative Nature of Turkish
Büşra Marşan, Salih Furkan Akkurt, Muhammet Şen +7
In this study, we aim to offer linguistically motivated solutions to resolve the issues of the lack of representation of null morphemes, highly productive derivational processes, a…
A Dataset and BERT-based Models for Targeted Sentiment Analysis on Turkish Texts
M. Melih Mutlu, Arzucan Özgür
Targeted Sentiment Analysis aims to extract sentiment towards a particular target from a given text. It is a field that is attracting attention due to the increasing accessibility…
Balancing Methods for Multi-label Text Classification with Long-Tailed Class Distribution
Yi Huang, Buse Giledereli, Abdullatif Köksal +2
Multi-label text classification is a challenging task because it requires capturing label dependencies. It becomes even more challenging when class distribution is long-tailed. Res…
Cluster-based Mention Typing for Named Entity Disambiguation
Arda Çelebi, Arzucan Özgür
An entity mention in text such as "Washington" may correspond to many different named entities such as the city "Washington D.C." or the newspaper "Washington Post." The goal of na…
Vapur: A Search Engine to Find Related Protein-Compound Pairs in COVID-19 Literature
Abdullatif Köksal, Hilal Dönmez, Rıza Özçelik +2
Coronavirus Disease of 2019 (COVID-19) created dire consequences globally and triggered an intense scientific effort from different domains. The resulting publications created a hu…
A Hybrid Approach to Dependency Parsing: Combining Rules and Morphology with Deep Learning
Şaziye Betül Özateş, Arzucan Özgür, Tunga Güngör +1
Fully data-driven, deep learning-based models are usually designed as language-independent and have been shown to be successful for many natural language processing tasks. However,…
Exploring Chemical Space using Natural Language Processing Methodologies for Drug Discovery
Hakime Öztürk, Arzucan Özgür, Philippe Schwaller +2
Text-based representations of chemicals and proteins can be thought of as unstructured languages codified by humans to describe domain-specific knowledge. Advances in natural langu…
Resources for Turkish Dependency Parsing: Introducing the BOUN Treebank and the BoAT Annotation Tool
Utku Türk, Furkan Atmaca, Şaziye Betül Özateş +6
In this paper, we introduce the resources that we developed for Turkish dependency parsing, which include a novel manually annotated treebank (BOUN Treebank), along with the guidel…
WideDTA: prediction of drug-target binding affinity
Hakime Öztürk, Elif Ozkirimli, Arzucan Özgür
Motivation: Prediction of the interaction affinity between proteins and compounds is a major challenge in the drug discovery process. WideDTA is a deep-learning based prediction mo…
ChemBoost: A chemical language based approach for protein-ligand binding affinity prediction
Rıza Özçelik, Hakime Öztürk, Arzucan Özgür +1
Identification of high affinity drug-target interactions is a major research question in drug discovery. Proteins are generally represented by their structures or sequences. Howeve…
Named Entity Recognition on Twitter for Turkish using Semi-supervised Learning with Word Embeddings
Eda Okur, Hakan Demir, Arzucan Özgür
Recently, due to the increasing popularity of social media, the necessity for extracting information from informal text types, such as microblog texts, has gained significant atten…
A novel methodology on distributed representations of proteins using their interacting ligands
Hakime Öztürk, Elif Ozkirimli, Arzucan Özgür
The effective representation of proteins is a crucial task that directly affects the performance of many bioinformatics problems. Related proteins usually bind to similar ligands.…
DeepDTA: Deep Drug-Target Binding Affinity Prediction
Hakime Öztürk, Elif Ozkirimli, Arzucan Özgür
The identification of novel drug-target (DT) interactions is a substantial part of the drug discovery process. Most of the computational methods that have been proposed to predict…