Oleksandr Narykov

According to our database, Oleksandr Narykov authored at least 7 papers between 2019 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Benchmarking community drug response prediction models: datasets, models, tools, and metrics for cross-dataset generalization analysis.

Alexander Partin, Priyanka Vasanthakumari, Oleksandr Narykov, Andreas Wilke, Natasha Koussa, Sara E. Jones, Yitan Zhu, Jamie C. Overbeek, Rajeev Jain, Gayara Demini Fernando, Cesar Sanchez-Villalobos, Cristina Garcia-Cardona, Jamaludin Mohd-Yusof, Nicholas Chia, Justin M. Wozniak, Souparno Ghosh, Ranadip Pal, Thomas S. Brettin, M. Ryan Weil, Rick L. Stevens

CoRR, March, 2025

Data imbalance in drug response prediction: multi-objective optimization approach in deep learning setting.

Oleksandr Narykov, Yitan Zhu, Thomas S. Brettin, Yvonne A. Evrard, Alexander Partin, Fangfang Xia, Maulik Shukla, Priyanka Vasanthakumari, James H. Doroshow, Rick L. Stevens

Briefings Bioinform., 2025

2024

Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry.

Yoel Zimmermann, Adib Bazgir, Zartashia Afzal, Fariha Agbere, Qianxiang Ai, Nawaf Alampara, Alexander Al-Feghali, Mehrad Ansari, Dmytro Antypov, Amro Aswad, Jiaru Bai, Viktoriia Baibakova, Devi Dutta Biswajeet, Erik Bitzek, Joshua D. Bocarsly, Anna S. Borisova, Andres M. Bran, L. Catherine Brinson, Marcel Moran Calderon, Alessandro Canalicchio, Victor Chen, Yuan Chiang, Defne Circi, Benjamin Charmes, Vikrant Chaudhary, Zizhang Chen, Min-Hsueh Chiu, Judith Clymo, Kedar Dabhadkar, Nathan Daelman, Archit Datar, Matthew L. Evans, Maryam Ghazizade Fard, Giuseppe Fisicaro, Abhijeet Sadashiv Gangan, Janine George, Jose D. Cojal Gonzalez, Michael Götte, Ankur K. Gupta, Hassan Harb, Pengyu Hong, Abdelrahman Ibrahim, Ahmed Ilyas, Alishba Imran, Kevin Ishimwe, Ramsey Issa, Kevin Maik Jablonka, Colin Jones, Tyler R. Josephson, Greg Juhasz, Sarthak Kapoor, Rongda Kang, Ghazal Khalighinejad, Sartaaj Khan, Sascha Klawohn, Suneel Kuman, Alvin Noe Ladines, Sarom Leang, Magdalena Lederbauer, Sheng-Lun Mark Liao, Hao Liu, Xuefeng Liu, Stanley Lo, Sandeep Madireddy, Piyush Ranjan Maharana, Shagun Maheshwari, Soroush Mahjoubi, José A. Márquez, Rob Mills, Trupti Mohanty, Bernadette Mohr, Seyed Mohamad Moosavi, Alexander Moßhammer, Amirhossein D. Naghdi, Aakash Naik, Oleksandr Narykov, Hampus Näsström, Xuan Vu Nguyen, Xinyi Ni, Dana O'Connor, Teslim Olayiwola, Federico Ottomano, Aleyna Beste Ozhan, Sebastian Pagel, Chiku Parida, Jaehee Park, Vraj Patel, Elena Patyukova, Martin Hoffmann Petersen, Luis Pinto, José M. Pizarro, Dieter Plessers, Tapashree Pradhan, Utkarsh Pratiush, Charishma Puli, Andrew Qin, Mahyar Rajabi, Francesco Ricci, Elliot Risch, Martiño Ríos-García

CoRR, 2024

Assessing Reusability of Deep Learning-Based Monotherapy Drug Response Prediction Models Trained with Omics Data.

Jamie C. Overbeek, Alexander Partin, Thomas S. Brettin, Nicholas Chia, Oleksandr Narykov, Priyanka Vasanthakumari, Andreas Wilke, Yitan Zhu, Austin Clyde, Sara Jones, Rohan Gnanaolivu, Yuanhang Liu, Jun Jiang, Chen Wang, Carter Knutson, Andrew D. McNaughton, Neeraj Kumar, Gayara Demini Fernando, Souparno Ghosh, Cesar Sanchez-Villalobos, Ruibo Zhang, Ranadip Pal, M. Ryan Weil, Rick L. Stevens

CoRR, 2024

2023

Entropy-Based Regularization on Deep Learning Models for Anti-Cancer Drug Response Prediction.

Oleksandr Narykov, Yitan Zhu, Thomas S. Brettin, Yvonne A. Evrard, Alexander Partin, Maulik Shukla, Priyanka Vasanthakumari, James H. Doroshow, Rick Stevens

Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, 2023

2022

Deep learning methods for drug response prediction in cancer: predominant and emerging trends.

Alexander Partin, Thomas S. Brettin, Yitan Zhu, Oleksandr Narykov, Austin Clyde, Jamie C. Overbeek, Rick L. Stevens

CoRR, 2022

2019

DISPOT: a simple knowledge-based protein domain interaction statistical potential.

Oleksandr Narykov, Dmytro Bogatov, Dmitry Korkin

Bioinform., 2019