5 accepted papers
Accurate prediction of compound protein interactions (CPIs) is crucial for drug discovery. However, existing deep learning-based methods suffer from hidden biases and poor cross-domain generalization, leading to spurious correlations and inadequate representation of unseen compound-protein pairs.
Recent genomic foundation models largely adopt large language model architectures that treat DNA as a one-dimensional token sequence. However, exhaustive sequential reading is structurally misaligned with sparse and discontinuous genomic semantics, leading to wasted computation on low-information ba…
During the past decade, DNA origami has emerged as a promising technology to construct DNA nanorobots with programmable configurations and excellent biocompatibility. However, existing DNA origami-based nanorobots exhibit weak stability in complex biological environments and lack efficient delivery
Speaker diarization aims to identify "who spoke when" in multi-person conversational scenarios. State-of-the-art audio-only diarization methods divide the task into multi-stages of speech segmentation, neural speaker embedding and unsupervised clustering. Egocentric speaker diarization (i.e., diariz…
Efficiently capturing consistent and complementary semantic features in context is crucial for Multimodal Emotion Recognition in Conversations (MERC). However, limited by the over-smoothing or low-pass filtering characteristics of spatial graph neural networks, are insufficient to accurately capture…