Aybar C. Acar

The paper identifies that lysosomal alkalinization under nutrient stress selects for metabolically plastic, motile cancer cells through mechanisms involving lysosomal exocytosis and sublethal apoptotic signaling.

The paper introduces MoCL-GAT, a self-supervised learning framework for molecular representation, achieving state-of-the-art performance on molecular property prediction tasks.

The paper identifies an exosomal miRNA signature related to EMT that predicts prognosis in hepatocellular carcinoma with moderate accuracy.

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

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.