5 citations · 10 across the 5 of their papers we have counts for
5 papers
Benchmarking community drug response prediction models: datasets, models, tools, and metrics for cross-dataset generalization analysis
Alexander Partin, Priyanka Vasanthakumari, Oleksandr Narykov +17
Deep learning (DL) and machine learning (ML) models have shown promise in drug response prediction (DRP), yet their ability to generalize across datasets remains an open question,…
Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
Yoel Zimmermann, Adib Bazgir, Zartashia Afzal +141
Here, we present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry, which engaged participants across global hyb…
Assessing Reusability of Deep Learning-Based Monotherapy Drug Response Prediction Models Trained with Omics Data
Jamie C. Overbeek, Alexander Partin, Thomas S. Brettin +21
Cancer drug response prediction (DRP) models present a promising approach towards precision oncology, tailoring treatments to individual patient profiles. While deep learning (DL)…
Influencing factors on false positive rates when classifying tumor cell line response to drug treatment
Priyanka Vasanthakumari, Thomas Brettin, Yitan Zhu +6
Informed selection of drug candidates for laboratory experimentation provides an efficient means of identifying suitable anti-cancer treatments. The advancement of artificial intel…
Deep learning methods for drug response prediction in cancer: predominant and emerging trends
Alexander Partin, Thomas S. Brettin, Yitan Zhu +4
Cancer claims millions of lives yearly worldwide. While many therapies have been made available in recent years, by in large cancer remains unsolved. Exploiting computational predi…