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Page 1
Insights into Dynamic Network States Using Metabolomic Data.
Mostolizadeh R, Dräger A, Jamshidi N. Mostolizadeh R, et al. Methods Mol Biol. 2019;1978:243-258. doi: 10.1007/978-1-4939-9236-2_15. Methods Mol Biol. 2019. PMID: 31119667
Metabolomic data is the youngest of the high-throughput data types; however, it is potentially one of the most informative, as it provides a direct, quantitative biochemical phenotype. ...Genome-scale metabolic network reconstructions provide a natural …
Metabolomic data is the youngest of the high-throughput data types; however, it is potentially one of the most informat …
Omics-Based Investigations of Breast Cancer.
Neagu AN, Whitham D, Bruno P, Morrissiey H, Darie CA, Darie CC. Neagu AN, et al. Molecules. 2023 Jun 14;28(12):4768. doi: 10.3390/molecules28124768. Molecules. 2023. PMID: 37375323 Free PMC article. Review.
Rapid and recent advances in molecular profiling strategies and analytical techniques based on high-throughput sequencing and mass spectrometry (MS) development have generated large-scale multi-omics datasets, mainly emerging from the three "big omics", based on the central dogma …
Rapid and recent advances in molecular profiling strategies and analytical techniques based on high-throughput sequencing and mass spectrome …
Cardiac Fibrosis in the Multi-Omics Era: Implications for Heart Failure.
Ghazal R, Wang M, Liu D, Tschumperlin DJ, Pereira NL. Ghazal R, et al. Circ Res. 2025 Mar 28;136(7):773-802. doi: 10.1161/CIRCRESAHA.124.325402. Epub 2025 Mar 27. Circ Res. 2025. PMID: 40146800 Free PMC article. Review.
High-throughput omics technologies have begun revolutionizing our understanding of the molecular mechanisms driving cardiac fibrosis and are providing unprecedented insights into its heterogeneity and progression. This review provides a comprehensive analysis of how techni …
High-throughput omics technologies have begun revolutionizing our understanding of the molecular mechanisms driving cardiac fibrosis and are …
Global Burden of Cardiovascular Diseases and Risks, 1990-2022.
Mensah GA, Fuster V, Murray CJL, Roth GA; Global Burden of Cardiovascular Diseases and Risks Collaborators. Mensah GA, et al. J Am Coll Cardiol. 2023 Dec 19;82(25):2350-2473. doi: 10.1016/j.jacc.2023.11.007. J Am Coll Cardiol. 2023. PMID: 38092509 Free PMC article. No abstract available.
AI-driven multi-omics modeling of myalgic encephalomyelitis/chronic fatigue syndrome.
Xiong R, Aiken E, Caldwell R, Vernon SD, Kozhaya L, Gunter C, Bateman L, Unutmaz D, Oh J. Xiong R, et al. Nat Med. 2025 Sep;31(9):2991-3001. doi: 10.1038/s41591-025-03788-3. Epub 2025 Jul 25. Nat Med. 2025. PMID: 40715814 Free PMC article.
Here we present BioMapAI, a supervised deep neural network trained on a 4-year, longitudinal, multi-omics dataset from 249 participants, which integrates gut metagenomics, plasma metabolomics, immune cell profiling, blood laboratory data and detailed clinical …
Here we present BioMapAI, a supervised deep neural network trained on a 4-year, longitudinal, multi-omics dataset from 249 participan …
Plant engineering: advances, bottlenecks, and promise.
Brandizzi F, Mortimer J, Denby K. Brandizzi F, et al. Plant J. 2025 Apr;122(2):e70117. doi: 10.1111/tpj.70117. Plant J. 2025. PMID: 40275776
New tools revealed rich interconnectivity in the exponentially growing and dynamic "big 'omics data" of metabolomes, transcriptomes and genomes at previously inaccessible magnitude (global, cross-species, meta-) and resolution (single-cell). Insights e …
New tools revealed rich interconnectivity in the exponentially growing and dynamic "big 'omics data" of metabolomes, tr …
Integrated molecular and functional characterization of the intrinsic apoptotic machinery identifies therapeutic vulnerabilities in glioma.
Fernandez EG, Mai WX, Song K, Bayley NA, Kim J, Zhu H, Pioso M, Young P, Andrasz CL, Cadet D, Liau LM, Li G, Yong WH, Rodriguez FJ, Dixon SJ, Souers AJ, Li JJ, Graeber TG, Cloughesy TF, Nathanson DA. Fernandez EG, et al. Nat Commun. 2024 Nov 21;15(1):10089. doi: 10.1038/s41467-024-54138-9. Nat Commun. 2024. PMID: 39572533 Free PMC article.
This failure is, in part, due to a myriad of genetic alterations and the plasticity of cancer signaling networks. Functional profiling, which ascertains signaling dynamics, is an alternative method to anticipate drug responses. ...This work demonstrates how compleme …
This failure is, in part, due to a myriad of genetic alterations and the plasticity of cancer signaling networks. Functional profilin …
Systems biology for crop improvement.
Pazhamala LT, Kudapa H, Weckwerth W, Millar AH, Varshney RK. Pazhamala LT, et al. Plant Genome. 2021 Jul;14(2):e20098. doi: 10.1002/tpg2.20098. Epub 2021 May 5. Plant Genome. 2021. PMID: 33949787 Free article. Review.
In recent years, generation of large-scale data from genome, transcriptome, proteome, metabolome, epigenome, and others, has become routine in several plant species. Most of these datasets in different crop species, however, were studied independently and as a resul …
In recent years, generation of large-scale data from genome, transcriptome, proteome, metabolome, epigenome, and others, has b …
Dynamic functional network connectivity and its association with lipid metabolism in Alzheimer's disease.
Zang F, Liu X, Fan D, He C, Zhang Z, Xie C; Alzheimer's Disease Neuroimaging Initiative; Alzheimer's Disease Metabolomics Consortium. Zang F, et al. CNS Neurosci Ther. 2024 Sep;30(9):e70029. doi: 10.1111/cns.70029. CNS Neurosci Ther. 2024. PMID: 39302036 Free PMC article.
METHODS: Data from 242 AD spectrum subjects, including biological, neuroimaging, and general cognition, were obtained from the Alzheimer's Disease Neuroimaging Initiative for this cross-sectional study. ...CONCLUSION: The dysfunction of dFNC temporal-spatial pattern …
METHODS: Data from 242 AD spectrum subjects, including biological, neuroimaging, and general cognition, were obtained from the …
A Biological-Systems-Based Analyses Using Proteomic and Metabolic Network Inference Reveals Mechanistic Insights into Hepatic Lipid Accumulation: An IMI-DIRECT Study.
Atabaki NN, Coral DE, Pomares-Millan H, Smith K, Behjat HH, Koivula RW, Tura A, Miller H, Pinnick K, Agudelo L, Allin KH, Brown AA, Chabanova E, Chmura PJ, Jacobsen UP, Dawed AY, Elders PJM, Fernandez-Tajes JJ, Forgie IM, Haid M, Hansen TH, Hansen EL, Jones AG, Kokkola T, Kalamajski S, Mahajan A, McDonald TJ, McEvoy D, Muilwijk M, Tsirigos KD, Vangipurapu J, van Oort S, Vestergaard H, Adamski J, Beulens JW, Brunak S, Dermitzakis ET, Giordano GN, Gupta R, Hansen T, Hart LT, Hattersley AT, Hodson L, Laakso M, Loos RJF, Merino J, Ohlsson M, Pedersen O, Ridderstråle M, Ruetten H, Rutters F, Schwenk JM, Tomlinson J, Walker M, Yaghootkar H, Karpe F, McCarthy MI, Thomas EL, Bell JD, Mari A, Pavo I, Pearson ER, Viñuela A, Franks PW. Atabaki NN, et al. medRxiv [Preprint]. 2025 Jun 2:2025.06.02.25328773. doi: 10.1101/2025.06.02.25328773. medRxiv. 2025. PMID: 40502600 Free PMC article. Preprint.
RESEARCH DESIGN AND METHODS: We used Bayesian network analyses to quantify causal pathways linking adipose distribution, glycemia, and insulin dynamics with fatty liver using data from the IMI-DIRECT prospective cohort study. ...Out of 446 analysed proteins, …
RESEARCH DESIGN AND METHODS: We used Bayesian network analyses to quantify causal pathways linking adipose distribution, glycemia, an …
65 results