Multi-Omics Data
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Related Articles from SNS
SDM-Q: Cost-Aware Staged Decision-Making for Multi-Omics Classification with Deep Q-Learning
arXiv:2605.31014v1 Announce Type: new Abstract: Multi-omics data provide complementary molecular characterizations of disease phenotypes and play an important role in disease diagnosis and subtype classification in precision medicine. However, acquiring complete multi-omics profiles is expensive and time-consuming, while most existing deep learning methods assume full modality availability during inference, resulting in substantial redundancy and limited practicality in clinical settings. To...
SDM-Q: Cost-Aware Staged Decision-Making for Multi-Omics Classification with Deep Q-Learning
Announce Type: replace Abstract: Multi-omics data provide complementary molecular characterizations of disease phenotypes and play an important role in disease diagnosis and subtype classification in precision medicine. However, acquiring complete multi-omics profiles is expensive and time-consuming, while most existing deep learning methods assume full modality availability during inference, resulting in substantial redundancy and limited practicality in clinical settings. To address this...
Interpretable Graph Kolmogorov-Arnold Networks for Multi-Cancer Classification and Biomarker Identification using Multi-Omics Data
arXiv:2503.22939v4 Announce Type: replace Abstract: The integration of heterogeneous multi-omics datasets at a systems level remains a central challenge for developing analytical and computational models in precision cancer diagnostics. This paper introduces Multi-Omics Graph Kolmogorov-Arnold Network (MOGKAN), a deep learning framework that utilizes messenger-RNA, micro-RNA sequences, and DNA methylation samples together with Protein-Protein Interaction (PPI) networks for cancer...
D&D-seq maps DNA-protein interactions in single cells with multi-omics compatibility
D&D-seq maps DNA-protein interactions in single cells with multi-omics compatibility Sadie Harley Scientific Editor Robert Egan Associate Editor A new technology allows scientists to map, in single cells, the DNA binding sites of transcription factors and other regulatory proteins that control gene activity, according to a study led by investigators at Weill Cornell Medicine and the New York Genome Center. With key advantages over methods currently in use, the technology is expected to be a...
A Pan-Cancer Multi-Omic SuperLearner for Regulated Cell Death Survival Topologies
Introduction: Regulated cell death (RCD) pathways profoundly influence tumor progression and immune modulation. In prior work, we constructed a comprehensive database mapping 25 forms of RCD across seven multi-omic layers encompassing 33 tumor types (CancerRCDShiny). Despite their robust ability to identify risk populations, translating these prognostic signatures into personalized clinical workflows requires a shift from generalized cohort stratification to individualized risk mapping.
Single-Cell Multi-Omics Dissection of Malignant Evolutionary Mechanisms and Construction of a Prognostic Model for Clear Cell Renal Cell Carcinoma
Clear cell renal cell carcinoma (ccRCC) exhibits pronounced heterogeneity across WHO histological grades, yet systematic single-cell multi-omics studies characterizing these transitions remain limited. We integrated scRNA-seq and scATAC-seq data across ccRCC WHO grades to establish a multi-omics framework encompassing tumor cells and immune populations. Using pseudotime trajectory analysis and machine learning ensembles, we developed a prognostic signature (CBG) from core nodes of...
Decoding Hierarchical Cell-Cell Communication in Spatial Multi-Omics with CellSTIC
Cell-cell communication helps to coordinate tissue development, homeostasis, and immune responses, but identifying signaling interactions within intact tissues remains difficult. Although single-cell transcriptomics has enabled systematic inference of ligand-receptor interactions, dissociation disrupts spatial context and limits the identification of bona fide local signaling and region-specific communication programs. Spatial transcriptomics and spatial multi-omics offer the opportunity to...
Extrachromosomal DNA as a Causal Instrument for Spatial Multi-Omics
Spatial transcriptomics, multiplex imaging, and computational pathology now map tissue organization at cellular resolution, but the analyses applied to these data remain correlational. Clustering and co-occurrence statistics describe which features appear together; they cannot say which feature drives the others. We propose a framework for causal inference in spatial multi-omics built on a specific feature of extrachromosomal DNA (ecDNA).
Sparse Functional Singular Value Decomposition for Biclustering and Triclustering Longitudinal Data
arXiv:2606.05488v1 Announce Type: cross Abstract: Identifying subtypes of complex conditions, such as Inflammatory Bowel Disease (IBD), often requires capturing latent patterns in longitudinal omics data. However, these data are typically high-dimensional, sparsely sampled, and irregularly observed over time, posing substantial challenges for conventional (bi)clustering and functional data analysis methods. We propose Tri-SfSVD, a unified sparse functional Singular Value Decomposition...
Mapping genetic risk mechanisms for immune-mediated diseases across human dendritic cell differentiation
Defining the cell types and mechanisms through which genetic variation operates is essential to understand the biological basis of disease. Although human dendritic cells (DCs) are crucial in regulating immunity, their rarity and limitations in available genomic data have hampered efforts to link inherited disease risk to specific DC subsets. Here, we present a single-cell multi-omic atlas of human DC differentiation from hematopoietic stem and progenitor cells (HSPCs) that includes...