Showing 1 - 20 results of 471 for search '"Multiomics"', query time: 0.07s Refine Results
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    Spatially Resolved Multiomics: Data Analysis from Monoomics to Multiomics by Changxiang Huan, Jinze Li, Yingxue Li, Shasha Zhao, Qi Yang, Zhiqi Zhang, Chuanyu Li, Shuli Li, Zhen Guo, Jia Yao, Wei Zhang, Lianqun Zhou

    Published 2025-01-01
    “…Spatial monoomics has been recognized as a powerful tool for exploring life sciences. Recently, spatial multiomics has advanced considerably, which could contribute to clarifying many biological issues. …”
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    Interpretable and integrative analysis of single-cell multiomics with scMKL by Samuel D. Kupp, Ian A. VanGordon, Mehmet Gönen, Sadık Esener, Sebnem Ece Eksi, Çiğdem Ak

    Published 2025-08-01
    “…In this manuscript, we introduce an innovative method for single-cell analysis using Multiple Kernel Learning (scMKL), that merges the predictive capabilities of complex models with the interpretability of linear approaches, aimed at providing actionable insights from single-cell multiomics data. scMKL excels at classifying healthy and cancerous cell populations across multiple cancer types, utilizing data from single-cell RNA sequencing, ATAC sequencing, and 10x Multiome. …”
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    Deconvolution of cell types and states in spatial multiomics utilizing TACIT by Khoa L. A. Huynh, Katarzyna M. Tyc, Bruno F. Matuck, Quinn T. Easter, Aditya Pratapa, Nikhil V. Kumar, Paola Pérez, Rachel J. Kulchar, Thomas J. F. Pranzatelli, Deiziane de Souza, Theresa M. Weaver, Xufeng Qu, Luiz Alberto Valente Soares Junior, Marisa Dolhnokoff, David E. Kleiner, Stephen M. Hewitt, Luiz Fernando Ferraz da Silva, Vanderson Geraldo Rocha, Blake M. Warner, Kevin M. Byrd, Jinze Liu

    Published 2025-04-01
    “…TACIT uses unbiased thresholding to distinguish positive cells from background, focusing on relevant markers to identify ambiguous cells in multiomic assays. Using five datasets (5,000,000 cells; 51 cell types) from three niches (brain, intestine, gland), TACIT outperforms existing unsupervised methods in accuracy and scalability. …”
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    Multiomics genetic insights into potential molecular targets for intracranial aneurysm by Shuo Wang, Xiaolin Chen, Yi Yang, Yitong Jia, Runting Li, Fa Lin

    “…Background This study aimed to identify multiomics therapeutic targets for aneurysmal subarachnoid haemorrhage (aSAH) and unruptured intracranial aneurysm (uIA) using Mendelian randomisation (MR), summary-data-based MR (SMR) and postanalysis methods.Methods Significant genetic variables were extracted from multiple databases, including Expression Quantitative Trait Loci (eQTL) from eQTLGen and Genotype-Tissue Expression V.8, protein QTL from eight plasma studies and methylation QTL from the 2018 genome-wide methylation study. …”
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    Unleashing the Power of Multiomics: Unraveling the Molecular Landscape of Peripheral Neuropathy by Julie Choi, Zitian Tang, Wendy Dong, Jenna Ulibarri, Elvisa Mehinovic, Simone Thomas, Ahmet Höke, Sheng Chih Jin

    Published 2025-04-01
    “…Furthermore, we discuss the clinical implications of genomic and multiomic integration, highlighting their potential to enhance diagnostic accuracy, prognostic assessment, and personalized treatment strategies for PN. …”
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    Integrated multiomics signatures to optimize the accurate diagnosis of lung cancer by Mengmeng Zhao, Gang Xue, Bingxi He, Jiajun Deng, Tingting Wang, Yifan Zhong, Shenghui Li, Yang Wang, Yiming He, Tao Chen, Jun Zhang, Ziyue Yan, Xinlei Hu, Liuning Guo, Wendong Qu, Yongxiang Song, Minglei Yang, Guofang Zhao, Bentong Yu, Minjie Ma, Lunxu Liu, Xiwen Sun, Yunlang She, Dan Xie, Deping Zhao, Chang Chen

    Published 2025-01-01
    “…In this multi-institutional study involving 2032 participants with IPLs, we integrate the clinical, radiomic with circulating cell-free DNA fragmentomic features in 5-methylcytosine (5mC)-enriched regions to establish a multiomics model (clinic-RadmC) for predicting the malignancy risk of IPLs. …”
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