المؤلفون: Zhang, Zhiqian, Sie, Brandon, Chang, Aiquan, Leng, Yumei, Nardone, Christopher, Timms, Richard T., Elledge, Stephen J.

المصدر: Molecular Cell ; volume 83, issue 22, page 4191-4192 ; ISSN 1097-2765

مصطلحات موضوعية: Cell Biology, Molecular Biology

الإتاحة: https://doi.org/10.1016/j.molcel.2023.10.021Test
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المؤلفون: Nardone, Christopher, Palanski, Brad A., Scott, Daniel C., Timms, Richard T., Barber, Karl W., Gu, Xin, Mao, Aoyue, Leng, Yumei, Watson, Emma V., Schulman, Brenda A., Cole, Philip A., Elledge, Stephen J.

المصدر: Molecular Cell ; volume 83, issue 1, page 57-73.e9 ; ISSN 1097-2765

مصطلحات موضوعية: Cell Biology, Molecular Biology

الإتاحة: https://doi.org/10.1016/j.molcel.2022.12.013Test
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المؤلفون: Micali, Giuseppe, Verzì, Anna Elisa, Musumeci, Maria Letizia, Nardone, Beatrice, Monfrecola, Giuseppe, Lacarrubba, Francesco

المصدر: Journal of Investigative Dermatology ; volume 143, issue 4, page 667-669.e2 ; ISSN 0022-202X

مصطلحات موضوعية: Cell Biology, Dermatology, Molecular Biology, Biochemistry

الإتاحة: https://doi.org/10.1016/j.jid.2022.10.003Test
https://api.elsevier.com/content/article/PII:S0022202X22026562?httpAccept=text/xmlTest
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المؤلفون: Daftary, K.M., Feissinger, L., Nardone, B., Liszewski, W.

المصدر: Journal of Investigative Dermatology ; volume 142, issue 8, page B9 ; ISSN 0022-202X

مصطلحات موضوعية: Cell Biology, Dermatology, Molecular Biology, Biochemistry

الإتاحة: https://doi.org/10.1016/j.jid.2022.05.923Test
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المؤلفون: Emge, D.A., Liu, B., Green, C., Banez, L., Mauskar, M., Ziemer, C., Micheletti, R., Nutan, F., DeNiro, K., Mostaghimi, A., Keller, J., Nardone, B., Nguyen, C., Seminario-Vidal, L., Curtis, J., Madigan, L., R. deShazo, Cardones, A.

المصدر: Journal of Investigative Dermatology ; volume 142, issue 8, page S31 ; ISSN 0022-202X

مصطلحات موضوعية: Cell Biology, Dermatology, Molecular Biology, Biochemistry

الإتاحة: https://doi.org/10.1016/j.jid.2022.05.190Test
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المؤلفون: Lu, Stephen, Tirloni, Lucas, Oliveira, Markus Berger, Bosio, Christopher F., Nardone, Glenn A., Zhang, Yixiang, Hinnebusch, B. Joseph, Ribeiro, José M., Andersen, John F.

المساهمون: National Institute of Allergy and Infectious Diseases, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health

المصدر: Journal of Biological Chemistry ; volume 297, issue 5, page 101322 ; ISSN 0021-9258

مصطلحات موضوعية: Cell Biology, Molecular Biology, Biochemistry

الإتاحة: https://doi.org/10.1016/j.jbc.2021.101322Test
https://api.elsevier.com/content/article/PII:S0021925821011285?httpAccept=text/xmlTest
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المؤلفون: Ghafari, G., Liszewski, W., Nardone, B.

المصدر: Journal of Investigative Dermatology ; volume 141, issue 5, page S65 ; ISSN 0022-202X

مصطلحات موضوعية: Cell Biology, Dermatology, Molecular Biology, Biochemistry

الإتاحة: https://doi.org/10.1016/j.jid.2021.02.396Test
https://api.elsevier.com/content/article/PII:S0022202X21006217?httpAccept=text/xmlTest
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المؤلفون: Ciaramella, Angelo, Nardone, Davide, Staiano, Antonino

المصدر: BMC Bioinformatics ; volume 21, issue S10 ; ISSN 1471-2105

مصطلحات موضوعية: Applied Mathematics, Computer Science Applications, Molecular Biology, Biochemistry, Structural Biology

الوصف: Background High throughput methods, in biological and biomedical fields, acquire a large number of molecular parameters or omics data by a single experiment. Combining these omics data can significantly increase the capability for recovering fine-tuned structures or reducing the effects of experimental and biological noise in data. Results In this work we propose a multi-view integration methodology (named FH -Clust) for identifying patient subgroups from different omics information (e.g., Gene Expression , Mirna Expression , Methylation ). In particular, hierarchical structures of patient data are obtained in each omic (or view) and finally their topologies are merged by consensus matrix. One of the main aspects of this methodology, is the use of a measure of dissimilarity between sets of observations, by using an appropriate metric. For each view, a dendrogram is obtained by using a hierarchical clustering based on a fuzzy equivalence relation with Łukasiewicz valued fuzzy similarity. Finally, a consensus matrix, that is a representative information of all dendrograms, is formed by combining multiple hierarchical agglomerations by an approach based on transitive consensus matrix construction. Several experiments and comparisons are made on real data (e.g., Glioblastoma, Prostate Cancer) to assess the proposed approach. Conclusions Fuzzy logic allows us to introduce more flexible data agglomeration techniques. From the analysis of scientific literature, it appears to be the first time that a model based on fuzzy logic is used for the agglomeration of multi-omic data. The results suggest that FH -Clust provides better prognostic value and clinical significance compared to the analysis of single-omic data alone and it is very competitive with respect to other techniques from literature.

الإتاحة: https://doi.org/10.1186/s12859-020-03567-6Test

المؤلفون: Nardone, B., Nasca, M., Liszewski, W., West, D.P., Micali, G.

المصدر: Journal of Investigative Dermatology ; volume 140, issue 7, page S58 ; ISSN 0022-202X

مصطلحات موضوعية: Cell Biology, Dermatology, Molecular Biology, Biochemistry

الإتاحة: https://doi.org/10.1016/j.jid.2020.03.445Test
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المؤلفون: Zamparelli, Marco Sanduzzi, Marrone, Giuseppe, Gasbarrini, Antonio, Grieco, Antonio, Miele, Luca, COMPARE, DEBORA, COCCOLI, PIETRO, ROCCO, ALBA, NARDONE, OLGA MARIA, NARDONE, GERARDO ANTONIO PIO

المساهمون: Zamparelli, Marco Sanduzzi, Compare, Debora, Coccoli, Pietro, Rocco, Alba, Nardone, OLGA MARIA, Marrone, Giuseppe, Gasbarrini, Antonio, Grieco, Antonio, Nardone, GERARDO ANTONIO PIO, Miele, Luca

مصطلحات موضوعية: Cardiovascular disease, Gut microbiota, NAFLD, Catalysi, Molecular Biology, Spectroscopy, Computer Science Applications1707 Computer Vision and Pattern Recognition, Physical and Theoretical Chemistry, Organic Chemistry, Inorganic Chemistry

الوصف: The prevalence of metabolic disorders, such as type 2 diabetes (T2D), obesity, and non-alcoholic fatty liver disease (NAFLD), which are common risk factors for cardiovascular disease (CVD), has dramatically increased worldwide over the last decades. Although dietary habit is the main etiologic factor, there is an imperfect correlation between dietary habits and the development of metabolic disease. Recently, research has focused on the role of the microbiome in the development of these disorders. Indeed, gut microbiota is implicated in many metabolic functions and an altered gut microbiota is reported in metabolic disorders. Here we provide evidence linking gut microbiota and metabolic diseases, focusing on the pathogenetic mechanisms underlying this association.

العلاقة: info:eu-repo/semantics/altIdentifier/wos/WOS:000382337900039; volume:17; issue:8; firstpage:1225; numberofpages:11; journal:INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES; http://hdl.handle.net/11588/656267Test; info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85016312583; http://www.mdpi.com/1422-0067/17/8/1225/pdfTest

الإتاحة: https://doi.org/10.3390/ijms17081225Test
http://hdl.handle.net/11588/656267Test
http://www.mdpi.com/1422-0067/17/8/1225/pdfTest