Relevant bibliographies by topics / Multi-omics pathway analysis

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Multi-omics pathway analysis'

Author: Grafiati
Published: 5 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Multi-omics pathway analysis"

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Jeon, Jaemin, Eon Yong Han, and Inuk Jung. "MOPA: An integrative multi-omics pathway analysis method for measuring omics activity." PLOS ONE 18, no. 3 (2023): e0278272. http://dx.doi.org/10.1371/journal.pone.0278272.

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Pathways are composed of proteins forming a network to represent specific biological mechanisms and are often used to measure enrichment scores based on a list of genes in means to measure their biological activity. The pathway analysis is a de facto standard downstream analysis procedure in most genomic and transcriptomic studies. Here, we present MOPA (Multi-Omics Pathway Analysis), which is a multi-omics integrative method that scores individual pathways in a sample wise manner in terms of enriched multi-omics regulatory activity, which we refer to mES (multi-omics Enrichment Score). The mE
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Griss, Johannes, Guilherme Viteri, Konstantinos Sidiropoulos, Vy Nguyen, Antonio Fabregat, and Henning Hermjakob. "ReactomeGSA - Efficient Multi-Omics Comparative Pathway Analysis." Molecular & Cellular Proteomics 19, no. 12 (2020): 2115–24. http://dx.doi.org/10.1074/mcp.tir120.002155.

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Pathway analyses are key methods to analyze 'omics experiments. Nevertheless, integrating data from different 'omics technologies and different species still requires considerable bioinformatics knowledge.Here we present the novel ReactomeGSA resource for comparative pathway analyses of multi-omics datasets. ReactomeGSA can be used through Reactome's existing web interface and the novel ReactomeGSA R Bioconductor package with explicit support for scRNA-seq data. Data from different species is automatically mapped to a common pathway space. Public data from ExpressionAtlas and Single Cell Expre
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Wieder, Cecilia, Juliette Cooke, Clement Frainay, et al. "PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration." PLOS Computational Biology 20, no. 3 (2024): e1011814. http://dx.doi.org/10.1371/journal.pcbi.1011814.

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As terabytes of multi-omics data are being generated, there is an ever-increasing need for methods facilitating the integration and interpretation of such data. Current multi-omics integration methods typically output lists, clusters, or subnetworks of molecules related to an outcome. Even with expert domain knowledge, discerning the biological processes involved is a time-consuming activity. Here we propose PathIntegrate, a method for integrating multi-omics datasets based on pathways, designed to exploit knowledge of biological systems and thus provide interpretable models for such studies.
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Li, Feng, Tan Wu, Yanjun Xu, et al. "A comprehensive overview of oncogenic pathways in human cancer." Briefings in Bioinformatics 21, no. 3 (2019): 957–69. http://dx.doi.org/10.1093/bib/bbz046.

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Abstract Alterations of biological pathways can lead to oncogenesis. An overview of these oncogenic pathways would be highly valuable for researchers to reveal the pathogenic mechanism and develop novel therapeutic approaches for cancers. Here, we reviewed approximately 8500 literatures and documented experimentally validated cancer-pathway associations as benchmarking data set. This data resource includes 4709 manually curated relationships between 1557 paths and 49 cancers with 2427 upstream regulators in 7 species. Based on this resource, we first summarized the cancer-pathway associations
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Lan, Yiheng, Ruikun Sun, Jian Ouyang, et al. "AtMAD: Arabidopsis thaliana multi-omics association database." Nucleic Acids Research 49, no. D1 (2020): D1445—D1451. http://dx.doi.org/10.1093/nar/gkaa1042.

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Abstract Integration analysis of multi-omics data provides a comprehensive landscape for understanding biological systems and mechanisms. The abundance of high-quality multi-omics data (genomics, transcriptomics, methylomics and phenomics) for the model organism Arabidopsis thaliana enables scientists to study the genetic mechanism of many biological processes. However, no resource is available to provide comprehensive and systematic multi-omics associations for Arabidopsis. Here, we developed an Arabidopsis thaliana Multi-omics Association Database (AtMAD, http://www.megabionet.org/atmad), a
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Kim, Dokyoon, Ruowang Li, Anastasia Lucas, Shefali S. Verma, Scott M. Dudek, and Marylyn D. Ritchie. "Using knowledge-driven genomic interactions for multi-omics data analysis: metadimensional models for predicting clinical outcomes in ovarian carcinoma." Journal of the American Medical Informatics Association 24, no. 3 (2016): 577–87. http://dx.doi.org/10.1093/jamia/ocw165.

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It is common that cancer patients have different molecular signatures even though they have similar clinical features, such as histology, due to the heterogeneity of tumors. To overcome this variability, we previously developed a new approach incorporating prior biological knowledge that identifies knowledge-driven genomic interactions associated with outcomes of interest. However, no systematic approach has been proposed to identify interaction models between pathways based on multi-omics data. Here we have proposed such a novel methodological framework, called metadimensional knowledge-drive
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Sun, Pengcheng, Xing Liu, Yi Wang, et al. "Molecular characterization of allergic constitution based on network pharmacology and multi-omics analysis methods." Medicine 103, no. 7 (2024): e36892. http://dx.doi.org/10.1097/md.0000000000036892.

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The objective of this study was to identify critical pathways associated with allergic constitution. Shared genes among allergic rhinitis (AR), asthma (AA), and atopic dermatitis (AD) were extracted from the GWAS catalog. RNA-seq data of AR, AA, and AD from gene expression omnibus (GEO) database were preprocessed and subjected to differential gene expression analysis. The differentially expressed genes (DEGs) were merged using the Robust Rank Aggregation (RRA) algorithm. Weighted gene co-expression network analysis (WGCNA) was performed to identify modules associated with allergies. Components
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Wang, Yuhan. "High-Throughput Analysis of Core Pathways in HeLa Cells: Single-Cell Sequencing and AI-driven Modeling of Multi-Pathway Interaction Networks." Applied and Computational Engineering 178, no. 1 (2025): 47–57. https://doi.org/10.54254/2755-2721/2025.po25414.

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HeLa cells, the first successfully cultured human cancer cell line, are pivotal in cancer research, virology, and drug screening. However, their multi-omics heterogeneity and complex cancer-related cascades challenge traditional bulk sequencing, which fails to capture dynamic cell-cell interactions and resolve pathway crosstalk. This review systematically examines single-cell multi-omics technologies (transcriptomics, proteomics, and data integration) and AI-driven network modeling (graph neural networks, deep learning) for decoding HeLa cells' core pathways and metastasis mechanisms. It revea
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Lin, Dongdong, Hima B. Yalamanchili, Xinmin Zhang, et al. "CHOmics: A web-based tool for multi-omics data analysis and interactive visualization in CHO cell lines." PLOS Computational Biology 16, no. 12 (2020): e1008498. http://dx.doi.org/10.1371/journal.pcbi.1008498.

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Chinese hamster ovary (CHO) cell lines are widely used in industry for biological drug production. During cell culture development, considerable effort is invested to understand the factors that greatly impact cell growth, specific productivity and product qualities of the biotherapeutics. While high-throughput omics approaches have been increasingly utilized to reveal cellular mechanisms associated with cell line phenotypes and guide process optimization, comprehensive omics data analysis and management have been a challenge. Here we developed CHOmics, a web-based tool for integrative analysi
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Gallo Cantafio, Maria, Katia Grillone, Daniele Caracciolo, et al. "From Single Level Analysis to Multi-Omics Integrative Approaches: A Powerful Strategy towards the Precision Oncology." High-Throughput 7, no. 4 (2018): 33. http://dx.doi.org/10.3390/ht7040033.

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Integration of multi-omics data from different molecular levels with clinical data, as well as epidemiologic risk factors, represents an accurate and promising methodology to understand the complexity of biological systems of human diseases, including cancer. By the extensive use of novel technologic platforms, a large number of multidimensional data can be derived from analysis of health and disease systems. Comprehensive analysis of multi-omics data in an integrated framework, which includes cumulative effects in the context of biological pathways, is therefore eagerly awaited. This strategy
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Dissertations / Theses on the topic "Multi-omics pathway analysis"

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Guo, Weihua. "Computational Modeling of Planktonic and Biofilm Metabolism." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/79669.

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Most of microorganisms are ubiquitously able to live in both planktonic and biofilm states, which can be applied to dissolve the energy and environmental issues (e.g., producing biofuels and purifying waste water), but can also lead to serious public health problems. To better harness microorganisms, plenty of studies have been implemented to investigate the metabolism of planktonic and/or biofilm cells via multi-omics approaches (e.g., transcriptomics and proteomics analysis). However, these approaches are limited to provide the direct description of intracellular metabolism (e.g., metabolic
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Lu, Yingzhou. "Multi-omics Data Integration for Identifying Disease Specific Biological Pathways." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/83467.

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Pathway analysis is an important task for gaining novel insights into the molecular architecture of many complex diseases. With the advancement of new sequencing technologies, a large amount of quantitative gene expression data have been continuously acquired. The springing up omics data sets such as proteomics has facilitated the investigation on disease relevant pathways. Although much work has previously been done to explore the single omics data, little work has been reported using multi-omics data integration, mainly due to methodological and technological limitations. While a single om
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Books on the topic "Multi-omics pathway analysis"

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Suman, Shankar, Shivam Priya, and Akanksha Nigam, eds. Breast Cancer: Current Trends in Molecular Research. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/97816810895221120101.

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Breast cancer is one of the most common cancer types worldwide, and is a leading cause of cancer related deaths in women. In this book, medical experts review our current understanding of the molecular biology and characteristics of breast cancer. The topics covered in this book provide comprehensive knowledge of mechanisms underlying breast carcinogenesis, and are intended for a wide audience including scientists, teachers, and students. 11 chapters present information about several topics on breast cancer, including the role of cell growth and proliferation pathways, androgen and cytokine si
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Book chapters on the topic "Multi-omics pathway analysis"

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Eshun, Robert Benjamin, Hugette Naa Ayele Aryee, Marwan U. Bikdash, and A. K. M. Kamrul Islam. "Prediction and Analysis of Key Genes in Prostate Cancer via MRMR Enhanced Similarity Preserving Criteria and Pathway Enrichment Methods." In Machine Learning Methods for Multi-Omics Data Integration. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-36502-7_6.

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Maghsoudi, Zeynab, and Frederick C. Harris. "A Patient-Specific Multi-omics Pathway Analysis Method Using Hierarchical NNMF for Improved Gene Weighting." In Advances in Intelligent Systems and Computing. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-89063-5_63.

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B. Pathak, Anand, and Satyam Satyarthi. "Head Neck Squamous Cell Cancer Genomics: Oncogenes, Tumor Suppressor Genes and Clinical Implications." In Molecular Mechanisms in Cancer. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.101044.

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Head Neck Squamous Cell Cancer is genomically heterogenous. Common somatic mutations involve TP53, CDKN2A, FAT1, NOTCH1, PIK3CA, KMT2D and NSD1, less frequently others. Epigenetic changes also contribute to HNSCC biology. Alterations in tumor suppressor genes is a major oncogenic event in HNSCC. Genomic heterogeneity exists between different subsites within head neck region and also between the primary and metastatic disease. Intratumor heterogeneity has also been recognized. Based on key genomic alterations, four major molecular subtypes have been identified. Multi-omics analysis has provided
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Bhardwaj, Ankur, and Surendra Prakash Gupta. "Omics Integration in Understanding Xenobiotic Metabolism." In Cancer Exposomics and Environmental Influences on Carcinogenesis. IGI Global, 2025. https://doi.org/10.4018/979-8-3373-2165-3.ch002.

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The chapter explores the transformative role of omics technologies in elucidating the complex pathways and mechanisms involved in xenobiotic metabolism. The chapter incorporates the metabolomics role in profiling xenobiotic-induced metabolic alterations, facilitating the identification of biomarkers, transcriptomic analyses uncovering dynamic gene expression patterns, and the application of high-throughput sequencing techniques to identify genetic variations influencing drug metabolism enzymes and transporters, thereby shaping individual responses to xenobiotics. Integration of these omics dat
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Krishna Pasupuleti, Murali. "Quantum AI and Precision Genomics: Redefining Cancer Care and Personalized Medicine." In Precision Genomics and Quantum AI: Revolutionizing Cancer Treatment and Personalized Health. National Education Services, 2024. https://doi.org/10.62311/nesx/46080.

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Abstract: This chapter explores the transformative potential of Quantum Artificial Intelligence (Quantum AI) and precision genomics in revolutionizing cancer care and personalized medicine. Quantum AI combines the unparalleled computational power of quantum computing with advanced AI algorithms to analyze complex genomic datasets, accelerate drug discovery, and design patient-specific therapeutic strategies. Precision genomics, through next-generation sequencing and multi-omics data integration, enables the identification of genetic mutations, biomarkers, and cancer pathways, paving the way fo
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Chakilam, Chaitran. "Accelerating the discovery of disease mechanisms through deep learning and high-dimensional data analysis." In Deep Science Publishing. Deep Science Publishing, 2025. https://doi.org/10.70593/978-93-49307-36-0_4.

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The most critical next step to push the frontier of disease understanding is the discovery of the underlying mechanisms causing the large number of symptoms, sea of biomarkers and millions of damaged variable combinations of perturbation across individual patients. For most of these 6,000 diseases, medical scientists and practitioners have very limited knowledge and hypotheses regarding the mechanism and pathways involved. Studies to explore the disease mechanisms are usually conducted independently for a single disease or only consider a few diseases. Deep learning models and high-dimensional
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Meng, Wenjing, Jun Qin, Tixiao Wang, and Ruxing Zhao. "Application of Data Science in Management of Type 1 Diabetes." In Type 1 Diabetes - Causes, Treatments and Management [Working Title]. IntechOpen, 2025. https://doi.org/10.5772/intechopen.1009379.

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Data science has now significantly penetrated the realm of Type 1 diabetes (T1D) management, offering invaluable assistance to healthcare providers in forecasting, monitoring, and treating the disease. Leveraging computer technology, Data science enables real-time monitoring or even predication of patients’ blood sugar levels as well as complication development. By medical big data analytics involving patients’ blood sugar, dietary habits, exercise patterns, and other relevant information, it contributes to personalized follow-up plans that are tailored to each patient’s unique circumstances.
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Rodríguez, A., M. L. Castrejón-Godínez, P. Mussali-Galante, E. Tovar-Sánchez, and J. A. Díaz-Soto. "Microbial-mediated Pesticide Bioremediation: An Approach Through the OMIC Technologies." In Microbial Bioremediation and Multiomics Technologies for Sustainable Development. Royal Society of Chemistry, 2024. http://dx.doi.org/10.1039/bk9781837673131-00001.

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The worldwide use of pesticides has great benefits for agriculture. Pesticides offer crop protection from pests, enhance crop yields, and preserve the quality of agricultural products during storage, transport, and commercialization, generating great economic benefits for farmers. However, the extensive use of pesticides in agricultural activities is related to severe environmental pollution, mainly in soil and water bodies, constituting a menace to biodiversity, soil fertility, food supply, and human health. The use of biological systems such as microorganisms has been proposed as an effectiv
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Singh, Shubhra, and Douglas J. H. Shyu. "Metagenomics Insight Into Microbial Community Analysis During Pesticide Degradation: State of the Art, Success Stories, Challenges, and Future Outlook." In Microbial Bioremediation and Multiomics Technologies for Sustainable Development. Royal Society of Chemistry, 2024. http://dx.doi.org/10.1039/bk9781837673131-00481.

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Metagenomics has provided valuable insights into microbial community dynamics during pesticide degradation, revolutionizing our understanding of the complex interactions between microorganisms and pesticides in various ecosystems. Here, we will discuss the state of the art in metagenomics-based microbial community analysis during pesticide degradation, highlight success stories, address challenges, and explore future outlooks. Metagenomics approaches include amplicon sequencing, which targets specific genes to profile microbial communities, and shotgun metagenomics, which sequences all DNA fra
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Conference papers on the topic "Multi-omics pathway analysis"

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Sibilio, Pasquale, Federica Conte, and Paola Paci. "Beyond the network-based multi-omics data integration in COPD: a pathway-centric analysis." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822251.

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Klabunde, Björn, André Wesener, Wilhelm Bertrams, et al. "Multi-omics analysis reveals the NAD+ salvage pathway as defense mechanism against Streptococcus pneumoniae infection." In ERS Lung Science Conference 2022 abstracts. European Respiratory Society, 2022. http://dx.doi.org/10.1183/23120541.lsc-2022.209.

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Singh, Salendra, Hannah Gilmore, Maysa Abu-Khalaf, et al. "Abstract 419: Integrative analysis of multi-omics tumor profiles identifies pathways associated with resistance to anti-HER2 therapy in early stage breast cancer." In Proceedings: AACR Annual Meeting 2017; April 1-5, 2017; Washington, DC. American Association for Cancer Research, 2017. http://dx.doi.org/10.1158/1538-7445.am2017-419.

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