arXiv subjects

Jing Bao

Publications and source records attributed to Jing Bao.

2 recordsLinked to original sources

arXiv2026

Auto-Research Agent on MSA Organization to Improve Protein-Protein Complex Structure Prediction

Protein-complex structure prediction with AlphaFold 3 depends on MSA content, depth, and cross-chain pairing. Current pipelines apply uniform recipes, typically maximizing depth and pairing by species, yet even whether cross-chain pairing helps AF3 remains contested. We show that the value of both depth and pairing is strongly target-dependent, so no single recipe is optimal across interfaces. We introduce an autonomous agent that treats a frozen AF3 as a black-box experimental instrument. For each target, the agent formulates biological hypotheses, analyzes and reorganizes the MSA, and iteratively refines sequence selection and pairing using predictor label-free signals alone. On 1{,}015 protein--protein interfaces from three benchmarks released after AF3's training cutoff, the agent improves overall interface DockQ over AF3's default pipeline and fixed MSA recipes on every benchmark, while using on average 60\% fewer MSA sequences than the default. Gains are largest for inter-species heteromers, where species-based pairing is unreliable: mean DockQ increases from 0.465 to 0.612 on FoldBench and from 0.482 to 0.562 on HD439. The improvements arise from both sequence construction and pairing policy, including deliberate non-pairing. These results recast MSA design as a target-specific problem and show that an autonomous agent can adapt it at test time through biological reasoning and self-directed, confidence-guided experimentation.

cs.AI↗

arXiv2026

SpindleFlexNet: Flexible sleep spindles detection for EEG signals based on an adaptive one-dimensional RetinaNet-based framework

Sleep spindle is a physiologically significant biomedical signal in electroencephalographic (EEG) waveforms, which is typically a low-amplitude event in sleep. Due to the small signal ratio in the overall EEG, previous detection methods have limited capability to capture its start and end points and lack flexibility in handling multi-spindle scenarios. To address the gap, we address the problem from a new perspective and introduce SpindleFlexNet, the first framework in this field to apply deep learning-based one-dimensional object detection, leveraging an adapted one-dimensional RetinaNet architecture. The framework employs one-dimensional anchor generation, matching, and regression, along with a customized one-dimensional loss function. Analyses were conducted on two public datasets: the Montreal Archive of Sleep Studies and DREAMS, from which a total of 11,061 and 335 segments were obtained, respectively. When trained on these datasets, SpindleFlexNet achieved an average recall, precision, and F1-score of 0.61, 0.76, 0.67, and 0.58, 0.80, 0.67 in five-fold cross-validation. The model demonstrates stable detection performance and good generalization, making it a practical tool for sleep research. Potential applications include automated spindle labeling in clinical settings and as a reference for studies combining EEG with simultaneous functional magnetic resonance imaging.

eess.SP↗