Asthma is a heterogeneous disease where various subtypes are established and molecular biomarkers of the subtypes are yet to be discovered. Recent availability of multi-omics data paved a way to discover molecular biomarkers for the subtypes. However, multi-omics biomarker discovery is challenging because of the complex interplay between different omics layers. We propose a deep attention model named Gene-level biomarker discovery from multi-Omics data using graph ATtention neural network (GOAT) for identifying molecular biomarkers for eosinophilic asthma subtypes with multi-omics data. GOAT identifies genes that discriminate subtypes using a graph neural network by modeling complex interactions among genes as the attention mechanism in the deep learning model. In experiments with multi-omics profiles of the COREA (Cohort for Reality and Evolution of Adult Asthma in Korea) asthma cohort of 300 patients, GOAT outperforms existing models and suggests interpretable biological mechanisms underlying asthma subtypes. Importantly, GOAT identified genes that are distinct only in terms of relationship with other genes through attention. To better understand the role of biomarkers, we further investigated two transcription factors, CTNNB1 and JUN, captured by GOAT. We were successful in showing the role of the transcription factors in eosinophilic asthma pathophysiology in a network propagation and transcriptional network analysis, which were not distinct in terms of gene expression level differences. Source code is available https://github.com/DabinJeong/Multi-omics_biomarker. The preprocessed data underlying this article is accessible in data folder of the github repository. Raw data are available in Multi-Omics Platform at http://203.252.206.90:5566/, and it can be accessible when requested.

Understanding condition-specific biological mechanisms from RNA-seq data requires comprehensive analysis of gene expression data, from the gene to the network level. However, this requires computational expertise, which limits the accessibility of data analysis for understanding biological mechanisms. Therefore, the development of an easy-to-use and comprehensive analysis system is essential. In response to this issue, we present TFNetPropX, a user-friendly web-based platform designed to perform gene-level, gene-set-level, and network-level analysis of RNA-seq data under two different conditions. TFNetPropX performs comprehensive analysis, from DEG analysis to network propagation, to predict TF-affected genes with a single request, and provides users with an interactive web-based visualization of the results. To demonstrate the utility of our system, we performed analysis on two TF knockout RNA-seq datasets and effectively reproduced biologically significant findings. We believe that our system will make it easier for biological researchers to gain insights from different perspectives, allowing them to develop diverse hypotheses and analyses.

Go JR, et al. J Korean Med Sci. 2026 Nov;41:e276. https://doi.org/10.3346/jkms.2026.41.e276

Single-cell RNA sequencing (scRNA-seq) enables cellular characterization at single-cell resolution. However, its high dimensionality, sparsity, and noise m

Single-cell RNA sequencing has emerged as a transformative tool, enabling precise phenotype prediction and the detailed identification of disease-associated cell subpopulations. However, many existing computational approaches still rely on predefined cell-type annotations during model training. This dependence makes their predictive performance highly sensitive to subjective annotation quality, labeling inconsistencies, and dataset-specific biases, ultimately hindering their generalizability across diverse patient cohorts. To address these challenges, we propose scCap, an annotation-free framework that leverages knowledge-augmented clustering for robust phenotype prediction. Specifically, the framework first constructs initial clusters from raw gene expression profiles and subsequently refines them within the embedding space of a pretrained single-cell foundation model, allowing the clusters to better reflect broader biological organization while preserving fine-grained cellular heterogeneity. The resulting knowledge-augmented clusters are then integrated into a hierarchical multiple instance learning framework with dual-level attention, enabling interpretable predictions at both the cell and cluster levels. Evaluated across three public scRNA-seq datasets, scCap consistently outperforms baseline models in predictive accuracy. Furthermore, scCap identifies disease-associated subpopulations previously reported in the literature without relying on predefined cell-type annotations. These results demonstrate that scCap provides a robust and interpretable framework for annotation-free phenotype prediction.

Cell type annotation is essential for gaining biological insight from single-cell RNA sequencing data, yet manual labeling remains time-consuming and diffi