Serbülent Ünsal

Confidence Scoring for AI-Predicted Antibody-Antigen Complexes: AntiConf as a Precision-driven Metric

This paper evaluates AI methods for predicting antibody-antigen complexes, introduces AntiConf as a confidence metric, and shows it improves precision and recall for these predictions.

Evaluation of Methods for Protein Representation Learning: A Quantitative Analysis

This study evaluates protein representation learning methods across three tasks: semantic similarity, function prediction, and drug target classification, highlighting their strengths and limitations.

Personalized Tumor Growth Prediction with Multiscale Tumor Modeling

The paper develops a personalized tumor growth model using multiscale simulation and Bayesian methods, showing promising predictions with low error margins on A549 cell line data.