Search Results - "Omics data integration"

Bioinformatics approaches for integration and analysis of fungal omics data oriented to knowledge discovery and diagnosis

Authors: Hafez Khafaga, Ahmed Ibrahem

Contributors: University/Department: Universitat Pompeu Fabra. Departament de Ciències Experimentals i de la Salut

Thesis Advisors: Llorens, Carlos, Gabaldón Estevan, Juan Antonio

Source: TDX (Tesis Doctorals en Xarxa)

Subject Terms: Fungal omics, Bio-informatics pipeline/workflows, Omics data integration, Sequence analysis, Networks analysis, ömicas fúngicas, Pipelines bioinformáticos, Integración de datos ómicos, Análisis de secuencias, Análisis de redes

File Description: application/pdf

Access URL: http://hdl.handle.net/10803/671160

Comparative Analysis of Multi-Omics Integration Using Graph Neural Networks for Cancer Classification

Authors: Fadi Alharbi Aleksandar Vakanski Boyu Zhang et al.

Source: IEEE Access
IEEE Access, Vol 13, Pp 37724-37736 (2025)

Subject Terms: protein-protein interaction networks, multi-omics data integration, gene expression analysis, graph neural networks, Electrical engineering. Electronics. Nuclear engineering, Cancer classification, correlation matrices, Article, TK1-9971

Access URL: https://pubmed.ncbi.nlm.nih.gov/40123934
https://doaj.org/article/1a352378802f4808afa55f1094ff7a1a

Beyond the network-based multi-omics data integration in COPD: a pathway-centric analysis

Authors: Pasquale Sibilio Federica Conte Paola Paci

Source: 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). :6107-6112

Subject Terms: Network biology, multi-omics data integration, Pathway analysis, COPD

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Exclusive enteral nutrition initiates individual protective microbiome changes to induce remission in pediatric Crohn’s disease

Authors: Häcker, Deborah Siebert, Kolja Smith, Byron J et al.

Source: Cell Host & Microbe. 32(11)

Subject Terms: Medical Biochemistry and Metabolomics, Biological Sciences, Biomedical and Clinical Sciences, Microbiology, Prevention, Microbiome, Digestive Diseases, Autoimmune Disease, Pediatric, Crohn's Disease, Clinical Research, Genetics, Nutrition, Complementary and Integrative Health, Inflammatory Bowel Disease, 2.1 Biological and endogenous factors, Oral and gastrointestinal, Crohn Disease, Animals, Humans, Gastrointestinal Microbiome, Enteral Nutrition, Mice, Feces, Child, Female, Male, Germ-Free Life, Adolescent, Prospective Studies, Interleukin-10, Metagenomics, Fatty Acids, Disease Models, Animal, Remission Induction, Mice, Inbred C57BL, Clostridiales, Bacteria, EEN, FMT, bacterial strain dynamics, exclusive enteral nutrition, ex vivo gut chemostat model, fiber, medium-chain fatty acids, metagenomics, microbiome, multi-omics data integration, pediatric Crohn’s disease, Medical Microbiology, Immunology, Biochemistry and cell biology, Medical microbiology

Access URL: https://escholarship.org/uc/item/9hq7f43v

Interpretable graph Kolmogorov–Arnold networks for multi-cancer classification and biomarker identification using multi-omics data

Authors: Fadi Alharbi Nishant Budhiraja Aleksandar Vakanski et al.

Source: Scientific Reports, Vol 15, Iss 1, Pp 1-14 (2025)

Subject Terms: Cancer classification, Gene expression analysis, Multi-omics data integration, Kolmogorov–Arnold networks, Protein-protein interaction networks, Medicine, Science

File Description: electronic resource

Relation: https://doaj.org/toc/2045-2322

Access URL: https://doaj.org/article/07090f8098944bc1a48b6c36b09f6b34

A deep ensemble framework for human essential gene prediction by integrating multi-omics data

Authors: Xue Zhang Weijia Xiao Brent Cochran et al.

Source: Scientific Reports, Vol 15, Iss 1, Pp 1-11 (2025)

Subject Terms: Essential gene prediction, Deep learning, Snapshot ensemble, Multi-omics data integration, Medicine, Science

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Relation: https://doaj.org/toc/2045-2322

Access URL: https://doaj.org/article/9c49f4e9f0f4410fb7272acc4d56c248

Integrative analysis of multi-omics data and gut microbiota composition reveals prognostic subtypes and predicts immunotherapy response in colorectal cancer using machine learning

Authors: Jun Wang Yuan Cong Bo Tang et al.

Source: Scientific Reports, Vol 15, Iss 1, Pp 1-18 (2025)

Subject Terms: Colorectal cancer, Multi-omics data integration, Gut microbiota composition, Machine learning-based prognostic model, Immunotherapy response prediction, Medicine, Science

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Relation: https://doaj.org/toc/2045-2322

Access URL: https://doaj.org/article/cb94f9db75cf450b99a52ca27a3ba4c3

A systems biology approach to model COVID-19 phenotypes and drive drug discovery

Uma abordagem de biologia de sistemas para modelar fenótipos da COVID-19 e impulsionar a descoberta de fármacos

Authors: Oliveira, Alexandre Rafael Machado Dias, Oscar Rocha, Miguel et al.

Contributors: Oliveira, Alexandre Rafael Machado Dias, Oscar Rocha, Miguel et al.

Source: urn:tid:101814755

Subject Terms: COVID-19, SARS-CoV-2, Modelação metabólica, Integração de dados ómicos, Redes regulatórias, Redes de sinalização, Alvos de fármacos, Reutilização de fármacos, Metabolic modelling, Omics data integration, Transcriptional regulatory networks, Signalling networks, Drug targeting, Drug repurposing

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Availability: https://hdl.handle.net/1822/97975

Multi-omics analysis reveals important role for microbial-derived metabolites from Botryllus schlosseri in metal interactions

Authors: Dulce G. Guillén Matus Caroline M. Donaghy Nidhi Vijayan et al.

Source: mSystems, Vol 10, Iss 10 (2025)

Subject Terms: marine tunicates, Botryllus schlosseri, metallome, microbiome, metabolome and pan-metabolome, multi-omics data integration, Microbiology, QR1-502

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Relation: https://doaj.org/toc/2379-5077

Access URL: https://doaj.org/article/1cd1a1eb1b6e4709a959583a26f64a94

A Multi-Omics Approach to Liver Diseases: Integration of Single Nuclei Transcriptomics with Proteomics and HiCap Bulk Data in Human Liver

Authors: Cavalli, M. Diamanti, K. Pan, G. et al.

Source: Omics. 24(4):180-194

Subject Terms: human liver, multi-omics data integration, proteomics, snRNA-seq, ADAMTS protein, ADAMTS2 protein, albumin, biological marker, cadherin, cadherin 11, CD163 antigen, CD74 antigen, collagen type 3, collagen type 6, cystic fibrosis transmembrane conductance regulator, dihydropyrimidine dehydrogenase, epithelial cell adhesion molecule, fibroblast growth factor 1, fluorouracil, glucose transporter 1, glucose transporter 2, interleukin 18, messenger RNA, nostrin, proteome, small nuclear RNA, somatomedin binding protein 3, transcription factor Sox4, transcription factor Sox9, transcriptome, unclassified drug, vasculotropin receptor 1, apoa2 gene, Article, c3 gene, controlled study, dcn gene, enhancer region, eps2 gene, f5 gene, f8 gene, fgb gene, fgl1 gene, g6pc gene, gene, gene identification, human, human cell, human chromosome, human tissue, intron, lamb1 gene, liver cell carcinoma, liver parenchyma, lrp2 gene, marker gene, mass spectrometry, multiomics, mup3 gene, myo1f gene, oit3 gene, pck1 gene, plekhg1 gene, plg gene, priority journal, promoter region, ptprb gene, RNA conformation, RNA sequencing, single nucleotide polymorphism, spp1 gene, transcriptomics

File Description: print

Access URL: https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-277233
https://doi.org/10.1089/omi.2019.0215

Analytical aspects of metagenomic and volatilomic approaches that advance table olive integrity research

Authors: Despoina Langari Fani Th. Mantzouridou

Source: Exploration of Foods and Foodomics, Vol 2, Iss 6, Pp 570-598 (2024)

Subject Terms: metagenomics, amplicon metagenomics, volatilomics, table olives, omics data integration, food analysis, Nutrition. Foods and food supply, TX341-641, bioinformatics, TP368-456, fermentation, Food processing and manufacture

Access URL: https://doaj.org/article/fd3cfe9864bd433c9386cc31b163e41f

MOLUNGN: a multi-omics graph neural network for biomarker discovery and accurate lung cancer classification

Authors: Daifeng Zhang Guoqiang Bian Yuanbin Zhang et al.

Source: Front Genet
Frontiers in Genetics, Vol 16 (2025)

Subject Terms: lung cancer, multi-omics data integration, GAT, MOLUNGN, stage prediction, Genetics, QH426-470

Access URL: https://doaj.org/article/3ecd4692bfd44e8499316a6589f194c7

An integrated environmental toxicity risk assessment framework combining deep learning and molecular simulation: A case study on pyrethrins and breast cancer

Authors: Jinghui Sung Zikang Jiang Wen-Pei Sung et al.

Source: Biochemistry and Biophysics Reports, Vol 43, Iss , Pp 102141- (2025)

Subject Terms: Integrative computational toxicology, Multi-omics data integration, Deep learning for drug–target interaction, Multi-scale molecular simulation, Environmental chemical risk assessment, Molecular docking and dynamics, Biology (General), QH301-705.5, Biochemistry, QD415-436

File Description: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2405580825002286; https://doaj.org/toc/2405-5808

Access URL: https://doaj.org/article/d4039f2fece74ef38b151fe6bdd290c8

Identification of key drought-tolerant genes in soybean using an integrative data-driven feature engineering pipeline

Authors: Pei-Hsiu Kao Supaporn Baiya Chong-Wei Lee et al.

Source: Journal of Big Data, Vol 12, Iss 1, Pp 1-30 (2025)

Subject Terms: Soybean, Drought-tolerant responses, Data-driven feature engineering, Systems biology, Multi-dimensional omics and non-omics data integration, Key genes discovery, Computer engineering. Computer hardware, TK7885-7895, Information technology, T58.5-58.64, Electronic computers. Computer science, QA75.5-76.95

File Description: electronic resource

Relation: https://doaj.org/toc/2196-1115

Access URL: https://doaj.org/article/5a8d8f2eac234b9fb7236a7c0e027c27

Proteomics couples electrical remodelling to inflammation in a murine model of heart failure with sinus node dysfunction

Authors: Konstantin Kahnert Luca Soattin Robert W Mills et al.

Contributors: Konstantin Kahnert Luca Soattin Robert W Mills et al.

Source: Cardiovasc Res
Kahnert, K, Soattin, L, Mills, R W, Wilson, C, Maurya, S, Sorrentino, A, Al-Othman, S, Tikhomirov, R, van de Vegte, Y J, Hansen, F B, Achter, J, Hu, W, Zi, M, Smith, M, van der Harst, P, Olesen, M S, Olsen, K B, Banner, J, Jensen, T H L, Zhang, H, Boyett, M R, D'Souza, A & Lundby, A 2024, ' Proteomics couples electrical remodelling to inflammation in a murine model of heart failure with sinus node dysfunction ', Cardiovascular Research, vol. 120, no. 8, pp. 927–942 . https://doi.org/10.1093/cvr/cvae054

Subject Terms: Proteomics, Male, 0301 basic medicine, Potassium Channels, Sinus node dysfunction, Action Potentials, Heart failure, 03 medical and health sciences, 0302 clinical medicine, Heart Rate, Hyperpolarization-Activated Cyclic Nucleotide-Gated Channels, Galectin-3, Animals, Humans, Computer Simulation, Phosphorylation, Sinoatrial Node, Heart Failure, Sick Sinus Syndrome, Inflammation, Models, Cardiovascular, General Medicine, 3. Good health, Mice, Inbred C57BL, Multi-omics data integration, Disease Models, Animal, Original Article, Inflammation Mediators, Signal Transduction

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Access URL: https://pubmed.ncbi.nlm.nih.gov/38661182
https://dspace.library.uu.nl/handle/1874/455437
https://curis.ku.dk/ws/files/397607177/cvae054.pdf

Survival prediction and molecular subtyping of squamous cell lung cancer based on network embedding

Authors: Dingjie Guo Jing Chen Yixian Wang et al.

Source: Scientific Reports, Vol 14, Iss 1, Pp 1-13 (2024)

Subject Terms: Multi-omics data integration, Survival prediction, Molecular subtyping, Squamous cell lung cancer, Medicine, Science

File Description: electronic resource

Relation: https://doaj.org/toc/2045-2322

Access URL: https://doaj.org/article/5a35b635426d45168e6376488fab8811

Unveiling the molecular landscape of cognitive aging: insights from polygenic risk scores, DNA methylation, and gene expression

Authors: Sonya Neto Andreia Reis Miguel Pinheiro et al.

Source: Human Genomics, Vol 18, Iss 1, Pp 1-16 (2024)

Subject Terms: Cognitive aging, Cerebral small vessel disease (CSVD), Polygenic risk scores (PRS), Multi-omics data integration, Age-related methylation alterations, Age-related transcriptomic alterations, Medicine, Genetics, QH426-470

File Description: electronic resource

Relation: https://doaj.org/toc/1479-7364

Access URL: https://doaj.org/article/2e0c7763ac12478f9c1753c74e574d65

Pathway-Specific Insights into Colorectal Cancer Through Comprehensive Multi-Omics Data Integration

Authors: Tayyip Karaman Sinem Oktem Okullu Günseli Bayram Akçapınar et al.

Source: Biology ; Volume 14 ; Issue 5 ; Pages: 468

Subject Terms: multi-omics data integration, microbiome, transcriptome, metabolic pathways, colorectal cancer

Subject Geographic: agris

File Description: application/pdf

Relation: Bioinformatics; https://dx.doi.org/10.3390/biology14050468

Availability: https://doi.org/10.3390/biology14050468

Patient Similarity Networks Integration for Partial Multimodal Datasets

Authors: Gliozzo, Jessica Patak, Alex Puertas-Gallardo, Antonio et al.

Source: Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies. :228-234

Subject Terms: Patient Similarity Networks, Similarity Network Fusion, Data Integration, Multi-Omics Data Integration, Missing Data, Partial Samples

File Description: application/pdf

Access URL: https://hdl.handle.net/2434/957101
https://doi.org/10.5220/0011725500003414

Computational analysis of the functional impact of MHC-II-expressing triple-negative breast cancer

Authors: Yang Cui Weihang Zhang Xin Zeng et al.

Source: Front Immunol
Frontiers in Immunology, Vol 15 (2024)

Subject Terms: 0301 basic medicine, 0303 health sciences, Gene Expression Profiling, Immunology, Histocompatibility Antigens Class II, Computational Biology, Triple Negative Breast Neoplasms, RC581-607, Prognosis, Gene Expression Regulation, Neoplastic, 03 medical and health sciences, breast cancer, machine learning, multi-omics data integration, Tumor Microenvironment, Biomarkers, Tumor, tumor microenvironment, Humans, MHC-II pathway, Female, Immunologic diseases. Allergy, Transcriptome

Access URL: https://pubmed.ncbi.nlm.nih.gov/39664386
https://doaj.org/article/cb09e89bbe7f4e75982285fbd8bcbce3