Paper List
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Simulation and inference methods for non-Markovian stochastic biochemical reaction networks
This paper addresses the computational bottleneck of simulating and performing Bayesian inference for non-Markovian biochemical systems with history-d...
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Translating Measures onto Mechanisms: The Cognitive Relevance of Higher-Order Information
This review addresses the core challenge of translating abstract higher-order information theory metrics (e.g., synergy, redundancy) into defensible, ...
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Emergent Bayesian Behaviour and Optimal Cue Combination in LLMs
This paper addresses the critical gap in understanding whether LLMs spontaneously develop human-like Bayesian strategies for processing uncertain info...
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Vessel Network Topology in Molecular Communication: Insights from Experiments and Theory
This work addresses the critical lack of experimentally validated channel models for molecular communication within complex vessel networks, which is ...
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Modulation of DNA rheology by a transcription factor that forms aging microgels
This work addresses the fundamental question of how the transcription factor NANOG, essential for embryonic stem cell pluripotency, physically regulat...
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Imperfect molecular detection renormalizes apparent kinetic rates in stochastic gene regulatory networks
This paper addresses the core challenge of distinguishing genuine stochastic dynamics of gene regulatory networks from artifacts introduced by imperfe...
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Approximate Bayesian Inference on Mechanisms of Network Growth and Evolution
This paper addresses the core challenge of inferring the relative contributions of multiple, simultaneous generative mechanisms in network formation w...
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An AI Implementation Science Study to Improve Trustworthy Data in a Large Healthcare System
This paper addresses the critical gap between theoretical AI research and real-world clinical implementation by providing a practical framework for as...
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.