Multi-Omics
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Related Articles from SNS
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...
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...
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...
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).
scTranslation: A Comprehensive Benchmark for Single-Cell Multi-Omics Modality Translation
new Abstract: Simultaneous measurement of multiple omics modalities in single cells enables researchers to gain a more comprehensive understanding of cellular states and regulatory mechanisms. However, due to high experimental costs, significant noise, and incomplete modality coverage, a variety of computational methods for modality translation have emerged in recent years. Despite the development of translation models, there is still a lack of systematic benchmark evaluation in terms of...
Multi-omics analysis reveals chromatin and transcriptomic remodeling in hippocampal CA1 following adolescent social isolation
Social isolation (SI) during adolescence is associated with long-term vulnerability to psychiatric disorders; however, its effect on the hippocampal epigenome and transcriptome remains unclear. Here, we performed integrative ATAC-seq and RNA-seq of the hippocampal CA1 region using an adolescent mouse SI model, combined with single-cell RNA-seq reference mapping and cell type deconvolution. ATAC-seq identified SI-associated alterations in chromatin accessibility, including an increase in...