PathMoG: A Pathway-Centric Modular Graph Neural Network for Multi-Omics Survival Prediction

Cancer survival prediction from multi-omics data remains challenging because prognostic signals are high-dimensional, heterogeneous, and distributed across interacting genes and pathways. We propose PathMoG, a pathway-centric modular graph neural network for multi-omics survival prediction. PathMoG reorganizes genome-scale inputs into 354 KEGG-informed pathway modules, introduces a Hierarchical Omics Modulation module to condition gene-expression representations on mutation, copy number variation, pathway, and clinical context, and uses dual-level attention to capture both intra-pathway driver signals and inter-pathway clinical relevance. We evaluated PathMoG on 5,650 patients across 10 TCGA cancer types and observed consistent improvements over representative survival baselines. The framework further provides gene-level, pathway-level, and patient-level interpretability, supporting biologically grounded and clinically relevant risk stratification.

HBGSA: Hydrogen Bond Graph with Self-Attention for Drug-Target Binding Affinity Prediction

Accurate prediction of drug-target binding affinity accelerates drug discovery by prioritizing compounds for experimental validation. Current methods face three limitations: sequence-based approaches discard spatial geometric constraints, structure-based methods fail to exploit hydrogen bond features, and conventional loss functions neglect prediction-target correlation, a key factor for identifying high-affinity compounds in virtual screening. We developed HBGSA (Hydrogen Bond Graph with Self-Attention), a 3.06M-parameter model that encodes hydrogen bond spatial features. HBGSA uses graph neural networks to model hydrogen bond spatial topology with self-attention enhancement and Pearson correlation loss. Experimental results on PDBbind Core Set and CSAR-HiQ dataset demonstrate that HBGSA outperforms baseline methods with strong generalization capability. Ablation studies confirm the effectiveness of hydrogen bond modeling and Pearson correlation loss.

MEDNA-DFM: A Dual-View FiLM-MoE Model for Explainable DNA Methylation Prediction

Accurate computational identification of DNA methylation is essential for understanding epigenetic regulation. Although deep learning excels in this binary classification task, its "black-box" nature impedes biological insight. We address this by introducing a high-performance model MEDNA-DFM, alongside mechanism-inspired signal purification algorithms. Our investigation demonstrates that MEDNA-DFM effectively captures conserved methylation patterns, achieving robust distinction across diverse species. Validation on external independent datasets confirms that the model's generalization is driven by conserved intrinsic motifs (e.g., GC content) rather than phylogenetic proximity. Furthermore, applying our developed algorithms extracted motifs with significantly higher reliability than prior studies. Finally, empirical evidence from a Drosophila 6mA case study prompted us to propose a "sequence-structure synergy" hypothesis, suggesting that the GAGG core motif and an upstream A-tract element function cooperatively. We further validated this hypothesis via in silico mutagenesis, confirming that the ablation of either or both elements significantly degrades the model's recognition capabilities. This work provides a powerful tool for methylation prediction and demonstrates how explainable deep learning can drive both methodological innovation and the generation of biological hypotheses.

RC-GRPO: Reward-Conditioned Group Relative Policy Optimization for Multi-Turn Tool Calling Agents

Multi-turn tool calling is challenging for Large Language Models (LLMs) because rewards are sparse and exploration is expensive. A common recipe, SFT followed by GRPO, can stall when within-group reward variation is low (e.g., more rollouts in a group receive the all 0 or all 1 reward), making the group-normalized advantage uninformative and yielding vanishing updates. To address this problem, we propose RC-GRPO (Reward-Conditioned Group Relative Policy Optimization), which treats exploration as a controllable steering problem via discrete reward tokens. We first fine-tune a Reward-Conditioned Trajectory Policy (RCTP) on mixed-quality trajectories with reward goal special tokens (e.g., <|high_reward|>, <|low_reward|>) injected into the prompts, enabling the model to learn how to generate distinct quality trajectories on demand. Then during RL, we sample diverse reward tokens within each GRPO group and condition rollouts on the sampled token to improve within-group diversity, improving advantage gains. On the Berkeley Function Calling Leaderboard v4 (BFCLv4) multi-turn benchmark, our method yields consistently improved performance than baselines, and the performance on Qwen-2.5-7B-Instruct even surpasses all closed-source API models.

SCMPPI: Supervised Contrastive Multimodal Framework for Predicting Protein-Protein Interactions

Protein-Protein Interaction (PPI) prediction is a key task in uncovering cellular functional networks and disease mechanisms. However, traditional experimental methods are time-consuming and costly, and existing computational models face challenges in cross-modal feature fusion, robustness, and false-negative suppression. In this paper, we propose a novel supervised contrastive multimodal framework, SCMPPI, for PPI prediction. By integrating protein sequence features (AAC, DPC, CKSAAP-ESMC) with PPI network topology information (Node2Vec graph embedding), and combining an improved supervised contrastive learning strategy, SCMPPI significantly enhances PPI prediction performance. For the PPI task, SCMPPI introduces a negative sample filtering mechanism and modifies the contrastive loss function, effectively optimizing multimodal features. Experiments on eight benchmark datasets, including yeast, human, and H.pylori, show that SCMPPI outperforms existing state-of-the-art methods (such as DF-PPI and TAGPPI) in key metrics such as accuracy ( 98.01%) and AUC (99.62%), and demonstrates strong generalization in cross-species prediction (AUC > 99% on multi-species datasets). Furthermore, SCMPPI has been successfully applied to CD9 networks, the Wnt pathway, and cancer-specific networks, providing a reliable tool for disease target discovery. This framework also offers a new paradigm for multimodal biological information fusion and contrastive learning in collaborative optimization for various combined predictions.

PDeepPP:A Deep learning framework with Pretrained Protein language for peptide classification

Protein post-translational modifications (PTMs) and bioactive peptides (BPs) play critical roles in various biological processes and have significant therapeutic potential. However, identifying PTM sites and bioactive peptides through experimental methods is often labor-intensive, costly, and time-consuming. As a result, computational tools, particularly those based on deep learning, have become effective solutions for predicting PTM sites and peptide bioactivity. Despite progress in this field, existing methods still struggle with the complexity of protein sequences and the challenge of requiring high-quality predictions across diverse datasets. To address these issues, we propose a deep learning framework that integrates pretrained protein language models with a neural network combining transformer and CNN for peptide classification. By leveraging the ability of pretrained models to capture complex relationships within protein sequences, combined with the predictive power of parallel networks, our approach improves feature extraction while enhancing prediction accuracy. This framework was applied to multiple tasks involving PTM site and bioactive peptide prediction, utilizing large-scale datasets to enhance the model's robustness. In the comparison across 33 tasks, the model achieved state-of-the-art (SOTA) performance in 25 of them, surpassing existing methods and demonstrating its versatility across different datasets. Our results suggest that this approach provides a scalable and effective solution for large-scale peptide discovery and PTM analysis, paving the way for more efficient peptide classification and functional annotation.