Biology subjects

Ku, W.-S.

Publications and source records attributed to Ku, W.-S..

3 recordsLinked to original sources

bioRxiv2026

RiboRep: Replicate-Aware Cross-Modal Transformers for Codon-Resolved Ribosome Density Prediction

Ribosome profiling enables genome-wide measurement of translation at nucleotide resolution and provides a dynamic view of cellular protein synthesis under diverse biological conditions. Existing computational approaches primarily operate on codon-level representations, potentially losing fine-grained translational signals critical for modeling context-dependent cellular responses. Such predictive translational modeling is increasingly important for emerging biological digital twins, where accurate simulation of molecular-state dynamics is required to characterize cellular adaptation, perturbation response, and phenotype progression. We present RiboRep, a replicate-aware cross-modal transformer for codon-resolved ribosome density prediction. RiboRep jointly models nucleotide-resolution RNA sequences and reference ribosome occupancy signals using dual-stream convolutional encoders, RoPE-based self-attention, asymmetric cross-attention, and replicate-aware conditioning tokens. By explicitly modeling replicate-specific variation and integrating sequence context with experimentally observed translational activity, RiboRep provides a framework for reconstructing and simulating translational states across biological conditions. Across bacterial, yeast, and plant ribosome profiling datasets, RiboRep achieves competitive or improved performance compared with existing baselines, with particularly strong gains on replicate-rich plant datasets. Ablation studies further demonstrate the importance of local codon-aware feature extraction, replicate-aware conditioning, and gated readout. Beyond predictive performance, the proposed framework establishes a foundation for translation-aware molecular digital twins capable of modeling ribosome occupancy landscapes, perturbation-induced translational responses, and condition-specific regulatory programs at codon resolution. 1

bioinformatics↗

bioRxiv2026

PAG-Agent: a biologist-oriented research assistant for context-aware pathway-level analysis and interpretation

Pathway analysis is a critical step for translating gene-level omics results into biological mechanisms, yet existing workflows often leave researchers with long lists of statistically significant pathways that are difficult to interpret, validate, and connect to experimental context. We developed PAG-Agent, a biologist-oriented virtual research assistant that integrates pathway-level statistical analysis, context-aware biological interpretation, literature-supported reasoning, and scientific writing support within a unified workflow. PAG-Agent supports bulk and single-cell transcriptomic data and enables users to perform data preprocessing, differential expression analysis, pathway analysis, pathway-level consensus analysis, and pathway-level meta-analysis through click-based and chat-based interactions. Unlike conventional pathway-analysis tools that analyze gene sets largely in isolation, PAG-Agent incorporates experimental conditions and research objectives to prioritize biologically relevant pathways and generate interpretable hypotheses. The system also provides gene and pathway annotation, citation retrieval, visualization, and writing refinement functions. In Alzheimers disease case studies using three transcriptomic datasets, PAG-Agent consistently identified neurodegeneration-related pathways across multiple analysis methods and datasets. In citation-retrieval benchmarking, PAG-Agent outperformed six competing LLMs across five common literature-support scenarios, demonstrating improved ability to provide contextually relevant and valid references. Overall, PAG-Agent lowers technical barriers for pathway-level analysis and helps researchers move from transcriptomic data to biologically grounded interpretation, hypothesis generation, and scientific communication.

bioinformatics↗

bioRxiv2024

Toden-E: Topology-Based and Density-Based Ensembled Clustering for the Development of Super-PAG in Functional Genomics using PAG Network and LLM

The integrative analysis of gene sets, networks, and pathways is pivotal for deciphering omics data in translational biomedical research. To significantly increase gene coverage and enhance the utility of pathways, annotated gene lists, and gene signatures from diverse sources, we introduced pathways, annotated gene lists, and gene signatures (PAGs) enriched with metadata to represent biological functions. Furthermore, we established PAG-PAG networks by leveraging gene member similarity and gene regulations. However, in practice, high similarity in functional descriptions or gene membership often leads to redundant PAGs, hindering the interpretation from a fuzzy enriched PAG list. In this study, we developed todenE (topology-based and density-based ensemble) clustering, pioneering in integrating topology-based and density-based clustering methods to detect PAG communities leveraging the PAG network and Large Language Models (LLM). In computational genomics annotation, the genes can be grouped/clustered through the gene relationships and gene functions via guilt by association. Similarly, PAGs can be grouped into higher-level clusters, forming concise functional representations called Super-PAGs. TodenE captures PAG-PAG similarity and encapsulates functional information through LLM, in characterizing network-based functional Super-PAGs. In synthetic data, we introduced a metric called the Disparity Index (DI), measuring the connectivity of gene neighbors to gauge clusterability. We compared multiple clustering algorithms to identify the best method for generating performance-driven clusters. In non-simulated data (Gene Ontology), by leveraging transfer learning and LLM, we formed a language-based similarity embedding. TodenE utilizes this embedding together with the topology-based embedding to generate putative Super-PAGs with superior performance in semantic and gene member inclusiveness.

bioinformatics↗