1 code implementation • EMNLP (NLP-COVID19) 2020 • Abdullatif Köksal, Hilal Dönmez, Rıza Özçelik, Elif Ozkirimli, Arzucan Özgür
Coronavirus Disease of 2019 (COVID-19) created dire consequences globally and triggered an intense scientific effort from different domains.
no code implementations • 26 Oct 2022 • Asu Büşra Temizer, Gökçe Uludoğan, Rıza Özçelik, Taha Koulani, Elif Ozkirimli, Kutlu O. Ulgen, Nilgün Karalı, Arzucan Özgür
To this end, we build a language-inspired pipeline that treats high affinity ligands of protein targets as documents and selects key chemical words making up those ligands based on tf-idf weighting.
1 code implementation • 2 Sep 2022 • Gökçe Uludoğan, Elif Ozkirimli, Kutlu O. Ulgen, Nilgün Karalı, Arzucan Özgür
On the other hand, large amounts of unlabeled protein sequences and chemical compounds are available and have been used to train language models that learn useful representations.
no code implementations • 12 Nov 2021 • Igor Kulev, Berkay Köprü, Raul Rodriguez-Esteban, Diego Saldana, Yi Huang, Alessandro La Torraca, Elif Ozkirimli
The BioCreative VII Track 3 challenge focused on the identification of medication names in Twitter user timelines.
2 code implementations • EMNLP 2021 • Yi Huang, Buse Giledereli, Abdullatif Köksal, Arzucan Özgür, Elif Ozkirimli
Here, we introduce the application of balancing loss functions for multi-label text classification.
Ranked #1 on Multi-Label Text Classification on Reuters-21578
no code implementations • 5 Sep 2020 • Abdullatif Köksal, Hilal Dönmez, Rıza Özçelik, Elif Ozkirimli, Arzucan Özgür
Coronavirus Disease of 2019 (COVID-19) created dire consequences globally and triggered an intense scientific effort from different domains.
no code implementations • 10 Feb 2020 • Hakime Öztürk, Arzucan Özgür, Philippe Schwaller, Teodoro Laino, Elif Ozkirimli
Text-based representations of chemicals and proteins can be thought of as unstructured languages codified by humans to describe domain-specific knowledge.
no code implementations • 4 Feb 2019 • Hakime Öztürk, Elif Ozkirimli, Arzucan Özgür
In addition, the results showed that, given the protein sequence and ligand SMILES, the inclusion of protein domain and motif information as well as ligand maximum common substructure words do not provide additional useful information for the deep learning model.
1 code implementation • 2 Nov 2018 • Rıza Özçelik, Hakime Öztürk, Arzucan Özgür, Elif Ozkirimli
Our aim is to process the patterns in SMILES as a language to predict protein-ligand affinity, even when we cannot infer the function from the sequence.
no code implementations • 30 Jan 2018 • Hakime Öztürk, Elif Ozkirimli, Arzucan Özgür
We showed that ligand-based protein representation, which uses only SMILES strings of the ligands that proteins bind to, performs as well as protein-sequence based representation methods in protein clustering.
4 code implementations • 30 Jan 2018 • Hakime Öztürk, Elif Ozkirimli, Arzucan Özgür
The results show that the proposed deep learning based model that uses the 1D representations of targets and drugs is an effective approach for drug target binding affinity prediction.
Ranked #1 on Drug Discovery on BindingDB IC50