3 accepted papers
Current research in autonomous driving heavily relies on large-scale driving datasets for model fitting or trial-and-error learning strategies in simulation environments. However, these approaches suffer from limited behavioral diversity and fail to cover complex edge-case interactions. To address t
Identifying cancer driver genes remains challenging due to the complexity of gene interactions in cancer genomics. Existing methods often face difficulties in integrating multidimensional biological data, which limits their ability to capture diverse gene relationships. GATOmics, a novel multi-omics…
Accurate prediction of drug-target affinity plays a vital role in drug discovery and design. TriFP-NGram integrates multiple fingerprint and n-gram features to predict drug-target binding affinities. It surpasses current methods by leveraging a comprehensive set of molecular and protein features, en…