Mostrar el registro sencillo del ítem

Classification of mitochondrial network images associated with the study of breast cancer

dc.contributor.authorSilva, Jesússpa
dc.contributor.authorVarela Izquierdo, Noelspa
dc.contributor.authorDiaz Arroyo, Esperanzaspa
dc.contributor.authorPineda, Omarspa
dc.date.accessioned2020-11-11T16:41:48Z
dc.date.available2020-11-11T16:41:48Z
dc.date.issued2020
dc.identifier.issn2194-5357spa
dc.identifier.urihttps://hdl.handle.net/11323/7255spa
dc.description.abstractWithin various cellular processes, an increase in fission (a division of a single organelle into two or more independent structures) causes mitochondrial fragmentation and an increase in fusion (the opposite reaction of fission) produces a network of mitochondria that counteracts metabolic processes [1]. A balance between fission and fusion defines a mitochondrial morphology whose purpose is to meet metabolic demands and ensure removal of damaged organelles. These events have been associated with proliferation and redistribution of mitochondria, allowing the study of different breast cancer subtypes [2, 3]. This study presents a classification method for images of mitochondrial networks extracted from different cellular lines (MCF10A, BT549, MDAMB23, and CMF) belonging to different breast cancer subtypes.spa
dc.format.mimetypeapplication/pdfspa
dc.language.isoeng
dc.publisherCorporación Universidad de la Costaspa
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/spa
dc.sourceAdvances in Intelligent Systems and Computingspa
dc.subjectBreast cáncerspa
dc.subjectClassification of mitocondrialspa
dc.subjectNetwork imagesspa
dc.titleClassification of mitochondrial network images associated with the study of breast cancerspa
dc.typePre-Publicaciónspa
dc.source.urlhttps://www.scopus.com/record/display.uri?eid=2-s2.0-85089236108&doi=10.1007%2f978-3-030-51859-2_17&origin=inward&txGid=7f0968ee3e9c5c0311b8acf5d8e7b846spa
dc.rights.accessrightsinfo:eu-repo/semantics/closedAccessspa
dc.date.embargoEnd2021-05-07
dc.identifier.instnameCorporación Universidad de la Costaspa
dc.identifier.reponameREDICUC - Repositorio CUCspa
dc.identifier.repourlhttps://repositorio.cuc.edu.co/spa
dc.relation.referencesFang, Y., Zhao, J., Hu, L., Ying, X., Pan, Y., Wang, X.: Image classification toward breast cancer using deeply-learned quality features. J. Vis. Commun. Image Represent. 64, 102609 (2019)spa
dc.relation.referencesZhang, Z., Chen, L., Humphries, B., Brien, R., Wicha, M.S., Luker, K.E., Chen, Y.-C., Yoon, E.: Morphology-based prediction of cancer cell migration using an artificial neural network and a random decision forest. Integr. Biol. 10(12), 758–767 (2018)spa
dc.relation.referencesTerao, M., Goracci, L., Celestini, V., Kurosaki, M., Bolis, M., Di Veroli, A., Vallerga, A., Fratelli, M., Lupi, M., Corbelli, A., Fiordaliso, F.: Role of mitochondria and cardiolipins in growth inhibition of breast cancer cells by retinoic acid. J. Exp. Clin. Cancer Res. 38(1), 1–20 (2019)spa
dc.relation.referencesCaino, M.C., Seo, J.H., Aguinaldo, A., Wait, E., Bryant, K.G., Kossenkov, A.V., Hayden, J.E., Vaira, V., Morotti, A., Ferrero, S., Bosari, S.: A neuronal network of mitochondrial dynamics regulates metastasis. Nat. Commun. 7(1), 1–11 (2016)spa
dc.relation.referencesGonzalez, C.R., Woods, R.: Digital Image Processing, pp. 78–135. Prentice Hall, Upper Saddle River (2007)spa
dc.relation.referencesBishop, C.M.: Pattern Recognition and Machine Learning, pp. 423–428. Springer, Heidelberg (2006)spa
dc.relation.referencesIqbal, M.S., El-Ashram, S., Hussain, S., Khan, T., Huang, S., Mehmood, R., Luo, B.: Efficient cell classification of mitochondrial images by using deep learning. J. Opt. 48(1), 113–122 (2019)spa
dc.relation.referencesAggarwal, S., Gabrovsek, L., Langeberg, L.K., Golkowski, M., Ong, S.E., Smith, F.D., Scott, J.D.: Depletion of dAKAP1–protein kinase A signaling islands from the outer mitochondrial membrane alters breast cancer cell metabolism and motility. J. Biol. Chem. 294(9), 3152–3168 (2019)spa
dc.relation.referencesBindhu, V.: Biomedical image analysis using semantic segmentation. J. Innov. Image Process. (JIIP) 1(02), 91–101 (2019)spa
dc.relation.referencesEscala-Garcia, M., Abraham, J., Andrulis, I.L., Anton-Culver, H., Arndt, V., Ashworth, A., Auer, P.L., Auvinen, P., Beckmann, M.W., Behrens, S.: A network analysis to identify mediators of germline-driven differences in breast cancer prognosis. Nat. Commun. 11(1), 1–14 (2020)spa
dc.relation.referencesReis, Y., Bernardo-Faura, M., Richter, D., Wolf, T., Brors, B., Hamacher-Brady, A., Eils, R., Brady, N.R.: Multi-parametric analysis and modeling of relationships between mitochondrial morphology and apoptosis. PLoS ONE 7(1), e28694 (2012)spa
dc.relation.referencesHamacher-Brady, A., Stein, H.A., Turschner, S., Toegel, I., Mora, R., Jennewein, N., Efferth, T., Eils, R., Brady, N.R.: Artesunate activates mitochondrial apoptosis in breast cancer cells via iron-catalyzed lysosomal reactive oxygen species production. J. Biol. Chem. 286(8), 6587–6601 (2011)spa
dc.relation.referencesWang, L., Ward, J., Bouyea, M., Barroso, M.: Heterogeneity of mitochondria morphology in breast cancer cells. In: Multiscale Imaging and Spectroscopy, vol. 11216, p. 112160P. International Society for Optics and Photonics (2020) vspa
dc.relation.referencesDarvishi, K., Sharma, S., Bhat, A.K., Rai, E., Bamezai, R.N.K.: Mitochondrial DNA G10398A polymorphism imparts maternal Haplogroup N a risk for breast and esophageal cancer. Cancer Lett. 249(2), 249–255 (2007)spa
dc.relation.referencesCrudele, F., Bianchi, N., Reali, E., Galasso, M., Agnoletto, C., Volinia, S.: The network of non-coding RNAs and their molecular targets in breast cancer. Mol. Cancer 19(1), 1–18 (2020)spa
dc.relation.referencesJin, J., Lu, J.Q., Wen, Y., Tian, P., Hu, X.H.: Deep learning of diffraction image patterns for accurate classification of five cell types. J. Biophotonics 13(3), e201900242 (2020)spa
dc.relation.referencesVernier, M., Dufour, C.R., McGuirk, S., Scholtes, C., Li, X., Bourmeau, G., Giguère, V.: Estrogen-related receptors are targetable ROS sensors. Genes Dev. 34, 544–559 (2020)spa
dc.relation.referencesYang, W.S., Moon, H.G., Kim, H.S., Choi, E.J., Yu, M.H., Noh, D.Y., Lee, C.: Proteomic approach reveals FKBP4 and S100A9 as potential prediction markers of therapeutic response to neoadjuvant chemotherapy in patients with breast cancer. J. Proteome Res. 11(2), 1078–1088 (2012)spa
dc.relation.referencesEzzati, M., Yousefi, B., Velaei, K., Safa, A.: A review on anti-cancer properties of Quercetin in breast cancer. Life Sci. 117463 (2020)spa
dc.relation.referencesViloria, A., Acuña, G.C., Franco, D.J.A., Hernández-Palma, H., Fuentes, J.P., Rambal, E.P.: Integration of data mining techniques to PostgreSQL database manager system. Procedia Comput. Sci. 155, 575–580 (2019)spa
dc.relation.referencesSmolková, K., Bellance, N., Scandurra, F., Génot, E., Gnaiger, E., Plecitá-Hlavatá, L., Rossignol, R.: Mitochondrial bioenergetic adaptations of breast cancer cells to aglycemia and hypoxia. J. Bioenerg. Biomembr. 42(1), 55–67 (2010)spa
dc.relation.referencesSmolková, K., Bellance, N., Scandurra, F., Génot, E., Gnaiger, E., Plecitá-Hlavatá, L., Ježek, P., Rossignol, R.: Mitochondrial bioenergetic adaptations of breast cancer cells to aglycemia and hypoxia. J. Bioener. Biomembr. 42(1), 55–67 (2010)spa
dc.relation.referencesViloria, A., Lezama, O.B.P.: Improvements for determining the number of clusters in k-means for innovation databases in SMEs. ANT/EDI40, 1201–1206 (2019)spa
dc.relation.referencesHannemann, J., Velds, A., Halfwerk, J.B., Kreike, B., Peterse, J.L., van de Vijver, M.J.: Classification of ductal carcinoma in situ by gene expression profiling. Breast Cancer Res. 8(5), R61 (2006)spa
dc.relation.referencesDekker, T.J., Balluff, B.D., Jones, E.A., Schöne, C.D., Schmitt, M., Aubele, M., Kroep, J.R., Smit, V.T., Tollenaar, R.A., Mesker, W.E., Walch, A.: Multicenter matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) identifies proteomic differences in breast-cancer-associated stroma. J. Proteome Res. 13(11), 4730–4738 (2014)spa
dc.relation.referencesGiedt, R.J., Feruglio, P.F., Pathania, D., Yang, K.S., Kilcoyne, A., Vinegoni, C., Mitchison, T.J., Weissleder, R.: Computational imaging reveals mitochondrial morphology as a biomarker of cancer phenotype and drug response. Sci. Rep. 6, 32985 (2016)spa
dc.relation.referencesShermis, R.B., Wilson, K.D., Doyle, M.T., Martin, T.S., Merryman, D., Kudrolli, H., Brenner, R.J.: Supplemental breast cancer screening with molecular breast imaging for women with dense breast tissue. Am. J. Roentgenol. 207(2), 450–457 (2016)spa
dc.relation.referencesVarela, N., Silva, J., Gonzalez, F.M., Palencia, P., Palma, H.H., Pineda, O.B.: Method for the recovery of images in databases of rice grains from visual content. Procedia Comput. Sci. 170, 983–988 (2020)spa
dc.relation.referencesJones, M.M., Manwaring, N., Wang, J.J., Rochtchina, E., Mitchell, P., Sue, C.M.: Mitochondrial DNA haplogroups and age-related maculopathy. Arch. Ophthalmol. 125(9), 1235–1240 (2007)spa
dc.type.coarhttp://purl.org/coar/resource_type/c_816bspa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/preprintspa
dc.type.redcolhttp://purl.org/redcol/resource_type/ARTOTRspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.rights.coarhttp://purl.org/coar/access_right/c_14cbspa


Ficheros en el ítem

Thumbnail
Nombre:
CLASSIFICATION OF MITOCHONDRIAL ...
Tamaño:
6.261Kb
Formato:
PDF
Ver/
Thumbnail
Nombre:
license_rdf
Tamaño:
805bytes
Formato:
application/rdf+xml
Ver/

Este ítem aparece en la(s) siguiente(s) colección(ones)

  • Artículos científicos [3154]
    Artículos de investigación publicados por miembros de la comunidad universitaria.

Mostrar el registro sencillo del ítem

Attribution-NonCommercial-NoDerivatives 4.0 International
Excepto si se señala otra cosa, la licencia del ítem se describe como Attribution-NonCommercial-NoDerivatives 4.0 International