Chemical space exploration with quantum computing
Tag: machine learning
Rapid brain tumor classification from sparse epigenomic data
Although the intraoperative molecular diagnosis of the approximately 100 known brain tumor entities described to date has been a goal of neuropatho…
A machine learning approach to leveraging electronic health records for enhanced omics analysis
Omics studies produce a large number of measurements, enabling the development, validation and interpretation of systems-level biological models. L…
A unified evolution-driven deep learning framework for virus variation driver prediction
The increasing frequency of emerging viral infections necessitates a rapid human response, highlighting the cost-effectiveness of computational met…
A quantitative analysis of knowledge-learning preferences in large language models in molecular science
Deep learning has significantly advanced molecular modelling and design, enabling an efficient understanding and discovery of novel molecules. In p…
Evolutionary optimization of model merging recipes
Large language models (LLMs) have become increasingly capable, but their development often requires substantial computational resources. Although m…
A unified cross-attention model for predicting antigen binding specificity to both HLA and TCR molecules
The immune checkpoint inhibitors have demonstrated promising clinical efficacy across various tumour types, yet the percentage of patients who bene…
Discovering fully semantic representations via centroid- and orientation-aware feature learning
Learning meaningful representations of images in scientific domains that are robust to variations in centroids and orientations remains an importan…
Benchmarking AI-powered docking methods from the perspective of virtual screening
Recently, many artificial intelligence (AI)-powered protein–ligand docking and scoring methods have been developed, demonstrating impressive spee…
Towards a more inductive world for drug repurposing approaches
Drug–target interaction (DTI) prediction is a challenging albeit essential task in drug repurposing. Learning on graph models has drawn special a…