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multi-omics data set  (Omics Data Automation)

 
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    Omics Data Automation multi-omics data set
    Multi Omics Data Set, supplied by Omics Data Automation, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/multi+omics+data+set/multi+omics+data/pm34995810-43-16-13
    Average 90 stars, based on 1 article reviews
    multi-omics data set - by Bioz Stars, 2026-11
    90/100 stars

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    Related Articles

    Biomarker Discovery:

    Article Title: Machine learning combined with multi-omics to identify immune-related LncRNA signature as biomarkers for predicting breast cancer prognosis.
    Article Snippet: Machine learning combined with multi-omics to identify immunerelated LncRNA signature as biomarkers for predicting breast cancer prognosis Yuxing Liu1,9, Jintao Chen2,9, Daifeng Yang3,9, Chenming Liu4, Chunhui Tang5, Shanshan Cai6 & Yingxuan Huang7,8 This study developed an immune-related long non-coding RNAs (lncRNAs)-based prognostic signature by integrating multi-omics data and machine learning algorithms to predict survival and therapeutic responses in breast cancer patients.

    Article Title: Comprehensive machine learning analysis of PANoptosis signatures in multiple myeloma identifies prognostic and immunotherapy biomarkers
    Article Snippet: This study established the first PANoptosis-related prognostic model for multiple myeloma by integrating multi-omics data with machine learning algorithms.

    Article Title: Development and validation of an MBD3/UHRF1 methylation-regulator-based prognostic model for pancreatic cancer survival
    Article Snippet: In the future, we plan to incorporate more types of data (such as multi-omics data) and larger sample sizes to further refine and enhance the model’s performance.

    Article Title: Identification of matrix stiffness-related molecular subtypes in HCC via integrating multi-omics analysis and machine learning algorithms.
    Article Snippet: Highlights • The 10 clustering algorithms based on the multi-omics data and 101 machine learning algorithm combinations were used to construct the matrix stiffness-related signature to predict the prognosis and the immune landscape of HCC.

    Article Title: Machine learning combined with multi-omics to identify immune-related LncRNA signature as biomarkers for predicting breast cancer prognosis.
    Article Snippet: This study developed an immune-related long non-coding RNAs (lncRNAs)-based prognostic signature by integrating multi-omics data and machine learning algorithms to predict survival and therapeutic responses in breast cancer patients.

    Article Title: Machine learning identifies KRT8 dysregulation and endothelial remodeling in Moyamoya disease.
    Article Snippet: This study provides novel insights into the molecular mechanisms underlying MMD, and highlights promising drug targets through the integration of multi-omics data and machine learning approaches Materials and methods.

    Article Title: A Comprehensive Review of Deep Learning Applications with Multi-Omics Data in Cancer Research.
    Article Snippet: Academic Editor: Quan Zou Received: 25 April 2025 Revised: 24 May 2025 Accepted: 25 May 2025 Published: 28 May 2025 Citation: Sartori, F.; Codicè, F.; Caranzano, I.; Rollo, C.; Birolo, G.; Fariselli, P.; Pancotti, C. A Comprehensive Review of Deep Learning Applications with Multi-Omics Data in Cancer Research.

    Article Title: Recent advances in machine learning for precision diagnosis and treatment of esophageal disorders.
    Article Snippet: However, despite the improved capabilities of integrating multi-omics data, ML remains limited by heterogeneity and interpretability, setting its use to that of a supplementary tool to compliment conventional methods.



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    multi omics data set  (Omics Data Automation)
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    Omics Data Automation multi omics data set
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    Comprehensive overview of alterations in energy metabolism and lipid-handling pathways in patients with hypertrophic cardiomyopathy (HCM) vs nonfailing (NF) donors integrating metabolomics, <t>lipidomics,</t> and proteomics data. Metabolomic, lipidomic, and proteomic data were integrated to provide an overview of changes in major energy metabolism and lipid-handling pathways. Metabolites and lipids are indicated by objects with a dark fill and white text; proteins are indicated by objects with a light fill and black text. Blue, orange, and gray indicate lower, higher, and unchanged abundances in HCM vs NF donors, respectively. Created with BioRender.com .
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    quality data data set multi omics data sets data set correlation statistics references 53 63  (Omics Data Automation)
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    Comprehensive overview of alterations in energy metabolism and lipid-handling pathways in patients with hypertrophic cardiomyopathy (HCM) vs nonfailing (NF) donors integrating metabolomics, <t>lipidomics,</t> and proteomics data. Metabolomic, lipidomic, and proteomic data were integrated to provide an overview of changes in major energy metabolism and lipid-handling pathways. Metabolites and lipids are indicated by objects with a dark fill and white text; proteins are indicated by objects with a light fill and black text. Blue, orange, and gray indicate lower, higher, and unchanged abundances in HCM vs NF donors, respectively. Created with BioRender.com .
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    combined ad-metabolic multi-omics data set  (Omics Data Automation)
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    Seventeen predicted lipid species in the <t> AD multi‐omics data </t> sets with a VIP score >1 in the ABCA7‐KO cortical lipidome
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    multi-omics data set  (Omics Data Automation)
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    Seventeen predicted lipid species in the <t> AD multi‐omics data </t> sets with a VIP score >1 in the ABCA7‐KO cortical lipidome
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    Image Search Results


    Comprehensive overview of alterations in energy metabolism and lipid-handling pathways in patients with hypertrophic cardiomyopathy (HCM) vs nonfailing (NF) donors integrating metabolomics, lipidomics, and proteomics data. Metabolomic, lipidomic, and proteomic data were integrated to provide an overview of changes in major energy metabolism and lipid-handling pathways. Metabolites and lipids are indicated by objects with a dark fill and white text; proteins are indicated by objects with a light fill and black text. Blue, orange, and gray indicate lower, higher, and unchanged abundances in HCM vs NF donors, respectively. Created with BioRender.com .

    Journal: Circulation. Genomic and Precision Medicine

    Article Title: Integrating Clinical Phenotype With Multiomics Analyses of Human Cardiac Tissue Unveils Divergent Metabolic Remodeling in Genotype-Positive and Genotype-Negative Patients With Hypertrophic Cardiomyopathy

    doi: 10.1161/CIRCGEN.123.004369

    Figure Lengend Snippet: Comprehensive overview of alterations in energy metabolism and lipid-handling pathways in patients with hypertrophic cardiomyopathy (HCM) vs nonfailing (NF) donors integrating metabolomics, lipidomics, and proteomics data. Metabolomic, lipidomic, and proteomic data were integrated to provide an overview of changes in major energy metabolism and lipid-handling pathways. Metabolites and lipids are indicated by objects with a dark fill and white text; proteins are indicated by objects with a light fill and black text. Blue, orange, and gray indicate lower, higher, and unchanged abundances in HCM vs NF donors, respectively. Created with BioRender.com .

    Article Snippet: Processed metabolomics, lipidomics, and proteomics data are furthermore available as a Supplemental Material ( Multi-Omics Data Sets ).

    Techniques:

    Limited agreement among studies into metabolomic and lipidomic changes in tissue of patients with hypertrophic cardiomyopathy (HCM) compared with nonfailing (NF) donor tissue. A , Pie chart visualizing agreement in metabolite findings in HCM vs NF donor cardiac tissue samples; 114 metabolites were detected in at least 2 studies. Of these metabolites, 92 displayed inconsistent outcomes in HCM vs NF donor tissue. B , Table displaying consistency in lipidomic findings in HCM vs NF donor tissue. Blue and orange indicate consistently lowered and elevated levels, respectively, in HCM vs NF donors. *Derived from both metabolomics and lipidomics.

    Journal: Circulation. Genomic and Precision Medicine

    Article Title: Integrating Clinical Phenotype With Multiomics Analyses of Human Cardiac Tissue Unveils Divergent Metabolic Remodeling in Genotype-Positive and Genotype-Negative Patients With Hypertrophic Cardiomyopathy

    doi: 10.1161/CIRCGEN.123.004369

    Figure Lengend Snippet: Limited agreement among studies into metabolomic and lipidomic changes in tissue of patients with hypertrophic cardiomyopathy (HCM) compared with nonfailing (NF) donor tissue. A , Pie chart visualizing agreement in metabolite findings in HCM vs NF donor cardiac tissue samples; 114 metabolites were detected in at least 2 studies. Of these metabolites, 92 displayed inconsistent outcomes in HCM vs NF donor tissue. B , Table displaying consistency in lipidomic findings in HCM vs NF donor tissue. Blue and orange indicate consistently lowered and elevated levels, respectively, in HCM vs NF donors. *Derived from both metabolomics and lipidomics.

    Article Snippet: Processed metabolomics, lipidomics, and proteomics data are furthermore available as a Supplemental Material ( Multi-Omics Data Sets ).

    Techniques: Derivative Assay

    Seventeen predicted lipid species in the AD multi‐omics data sets with a VIP score >1 in the ABCA7‐KO cortical lipidome

    Journal: Journal of Neurochemistry

    Article Title: Pathway‐based integration of multi‐omics data reveals lipidomics alterations validated in an Alzheimer's disease mouse model and risk loci carriers

    doi: 10.1111/jnc.15719

    Figure Lengend Snippet: Seventeen predicted lipid species in the AD multi‐omics data sets with a VIP score >1 in the ABCA7‐KO cortical lipidome

    Article Snippet: Conditional cell‐type enrichment was performed on a combined AD‐metabolic multi‐omics data set to investigate enrichment relationships.

    Techniques: Biomarker Discovery