24 papers
SeekRBP is a novel sequence-structure framework that leverages reinforcement learning and a multi-armed bandit strategy to dynamically optimize negative sampling, thereby overcoming the limitations of traditional methods in accurately identifying receptor-binding proteins despite extreme sequence divergence and class imbalance.
This paper introduces a data-driven, neural-network-based approach that reduces the complexity of diverse microbiomes into a few interpretable functional groups, thereby linking specific microbial or genetic traits to community-wide functions and responses to perturbations across human and environmental health contexts.
This study demonstrates that a statistical mechanics model, combining a linear model with an Ising model and parametrized by in vitro binding energy measurements of the transcription factor Klf4, accurately predicts its genome-wide occupancy across the human genome without additional fitting parameters.
This study introduces causal circuit tracing to reveal that distinct single-cell foundation models (Geneformer and scGPT) share conserved computational architectures characterized by inhibitory dominance and biological coherence, with cross-model consensus identifying disease-associated domains that are validated by CRISPRi as reflecting co-expression rather than causal encoding.