The investigation of biological systems from different perspectives leads, due to novel -omics technologies, to large-scale, heterogeneous, and complex datasets. To elucidate molecular programs that control biological systems growth and development the integration and analysis of these -omics data remains challenging. Network-integrated visualizations based on graphical standards support intuitive exploration and interpretation of -omics data within the functional context. This integrated vision of the biological system to be studied tries to extract all hidden information for deepening our understanding and reveals new biological insights.The method described here gives detailed instructions on the generation of such an integrative visualization of -omics data in the context of networks presented in the Systems Biology Graphical Notation (SBGN) using VANTED; a software tool for systems biology applications. An example illustrates the application of the method for metabolomics and proteomics data integration and analysis using a primary metabolic pathway, for the model crop potato.
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http://dx.doi.org/10.1007/978-1-4939-7411-5_18 | DOI Listing |
Neurosurg Rev
January 2025
Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China.
Glioma is characterized by high heterogeneity and poor prognosis. Attempts have been made to understand its diversity in both genetic expressions and radiomic characteristics, while few integrated the two omics in predicting survival of glioma. This study was intended to investigate the connection between glioma imaging and genome, and examine its predictive value in glioma mortality risk and tumor immune microenvironment (TIME).
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Washington University School of Medicine, Saint Louis, MO, USA.
Background: The recent European-ancestry based genome-wide association study (GWAS) of Alzheimer disease (AD) by Bellenguez2022 has identified 75 significant genetic loci, but only a few have been functionally mapped to effector gene level. Besides the large-scale RNA expression, protein and metabolite levels are key molecular traits bridging the genetic variants to AD risk, and thus we decided to integrate them into the genetic analysis to pinpoint key proteins and metabolites underlying AD etiology. Few studies have generated more than one layer of post-transcriptional phenotypes, limiting the scale of biological translation of disease modifying treatments.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil.
Background: Positron emission tomography (PET) imaging greatly impacted Alzheimer's disease (AD) research and diagnosis. which makes predicting PET brain imaging alterations using blood data is of high interest. Additionally, integrating PET and omics data can provide new insights into AD pathophysiology.
View Article and Find Full Text PDFBrief Bioinform
November 2024
School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072 Shaanxi, China.
The identification of cancer driver genes is crucial for understanding the complex processes involved in cancer development, progression, and therapeutic strategies. Multi-omics data and biological networks provided by numerous databases enable the application of graph deep learning techniques that incorporate network structures into the deep learning framework. However, most existing methods do not account for the heterophily in the biological networks, which hinders the improvement of model performance.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Indiana University School of Medicine, Indianapolis, IN, USA.
Background: Genome-wide association studies (GWAS) identified the ATP binding cassette subfamily A member 7 (ABCA7) gene as increasing risk for Alzheimer's disease (AD). ABC proteins transport various molecules across extra and intra-cellular membranes. ABCA7 is part of the ABC1 subfamily and is expressed in brain cells including neurons, astrocytes, microglia, endothelial cells and pericytes.
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