Paper List
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PanFoMa: A Lightweight Foundation Model and Benchmark for Pan-Cancer
This paper addresses the dual challenge of achieving computational efficiency without sacrificing accuracy in whole-transcriptome single-cell represen...
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Beyond Bayesian Inference: The Correlation Integral Likelihood Framework and Gradient Flow Methods for Deterministic Sampling
This paper addresses the core challenge of calibrating complex biological models (e.g., PDEs, agent-based models) with incomplete, noisy, or heterogen...
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Contrastive Deep Learning for Variant Detection in Wastewater Genomic Sequencing
This paper addresses the core challenge of detecting viral variants in wastewater sequencing data without reference genomes or labeled annotations, ov...
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SpikGPT: A High-Accuracy and Interpretable Spiking Attention Framework for Single-Cell Annotation
This paper addresses the core challenge of robust single-cell annotation across heterogeneous datasets with batch effects and the critical need to ide...
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Unlocking hidden biomolecular conformational landscapes in diffusion models at inference time
This paper addresses the core challenge of efficiently and accurately sampling the conformational landscape of biomolecules from diffusion-based struc...
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Learning From Limited Data and Feedback for Cell Culture Process Monitoring: A Comparative Study
This paper addresses the core challenge of developing accurate real-time bioprocess monitoring soft sensors under severe data constraints: limited his...
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Cell-cell communication inference and analysis: biological mechanisms, computational approaches, and future opportunities
This review addresses the critical need for a systematic framework to navigate the rapidly expanding landscape of computational methods for inferring ...
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Generating a Contact Matrix for Aged Care Settings in Australia: an agent-based model study
This study addresses the critical gap in understanding heterogeneous contact patterns within aged care facilities, where existing population-level con...
STAR-GO: Improving Protein Function Prediction by Learning to Hierarchically Integrate Ontology-Informed Semantic Embeddings
Department of Computer Engineering, Bogazici University, Istanbul, Turkiye
The 30-Second View
IN SHORT: This paper addresses the core challenge of generalizing protein function prediction to unseen or newly introduced Gene Ontology (GO) terms by overcoming the limitations of existing models that either prioritize graph structure at the expense of semantic meaning or vice versa.
Innovation (TL;DR)
- Methodology Introduces a novel GO embedding module that integrates textual definitions (via SBERT-BioBERT) with ontology graph structure through a multi-task autoencoder, learning unified representations that preserve both semantic similarity and hierarchical dependencies.
- Methodology Proposes a hierarchical Transformer decoder that processes GO terms in topological order (ancestors to descendants) using causal self-attention, enabling information propagation across ontology levels and capturing functional dependencies.
- Biology Demonstrates superior zero-shot generalization to unseen GO terms, particularly for Molecular Function and Biological Process terms, by effectively leveraging semantic information from textual definitions, which transfers better to novel ontology concepts than purely structural embeddings.
Key conclusions
- STAR-GO achieves state-of-the-art or competitive performance across all three GO subontologies (BP, CC, MF), with the highest AUC scores (e.g., 0.989 for BP, 0.988 for CC, 0.995 for MF), indicating strong term-level discriminability.
- In zero-shot evaluation on 16 held-out GO terms, STAR-GO variants achieve the highest AUCs in 13 cases, significantly outperforming baselines like DeepGOZero and DeepGO-SE, demonstrating superior generalization to unseen functions.
- Ablation studies reveal that semantic embeddings (STAR_T) achieve the best zero-shot results for most MF and BP terms (e.g., AUC of 0.949 for GO:0001228), while structural embeddings (STAR_S) perform best for a few terms but poorly for MF, highlighting the critical role of semantic information for generalization.
Abstract: Motivation: Accurate prediction of protein function is essential for elucidating molecular mechanisms and advancing biological and therapeutic discovery. Yet experimental annotation lags far behind the rapid growth of protein sequence data. Computational approaches address this gap by associating proteins with Gene Ontology (GO) terms, which encode functional knowledge through hierarchical relations and textual definitions. However, existing models often emphasize one modality over the other, limiting their ability to generalize, particularly to unseen or newly introduced GO terms that frequently arise as the ontology evolves, and making the previously trained models outdated. Results: We present STAR-GO, a Transformer-based framework that jointly models the semantic and structural characteristics of GO terms to enhance zero-shot protein function prediction. STAR-GO integrates textual definitions with ontology graph structure to learn unified GO representations, which are processed in hierarchical order to propagate information from general to specific terms. These representations are then aligned with protein sequence embeddings to capture sequence–function relationships. STAR-GO achieves state-of-the-art performance and superior zero-shot generalization, demonstrating the utility of integrating semantics and structure for robust and adaptable protein function prediction. Availability: Code and pre-trained models are available at https://github.com/boun-tabi-lifelu/stargo.