Deep Manifold Transformation for Protein Representation Learning

arXiv:2402.09416v1 Announce Type: cross
Abstract: Protein representation learning is critical in various tasks in biology, such as drug design and protein structure or function prediction, which has primarily benefited from protein language models and graph neural networks. These models can capture intrinsic patterns from protein sequences and structures through masking and task-related losses. However, the learned protein representations are usually not well optimized, leading to performance degradation due to limited data, difficulty adapting to new tasks, etc. To address this, …

abstract arxiv biology cs.lg design drug design function graph graph neural networks intrinsic language language models losses manifold masking networks neural networks patterns prediction protein protein structure q-bio.bm representation representation learning tasks through transformation type