Classification of mitochondrial network images associated with the study of breast cancer
Within 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...
- Autores:
-
Silva, Jesús
Varela Izquierdo, Noel
Diaz Arroyo, Esperanza
Pineda, Omar
- Tipo de recurso:
- http://purl.org/coar/resource_type/c_816b
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7255
- Acceso en línea:
- https://hdl.handle.net/11323/7255
https://repositorio.cuc.edu.co/
- Palabra clave:
- Breast cáncer
Classification of mitocondrial
Network images
- Rights
- closedAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
| id |
RCUC2_33776bd0a1b0c7b9e71fd7359b206ec4 |
|---|---|
| oai_identifier_str |
oai:repositorio.cuc.edu.co:11323/7255 |
| network_acronym_str |
RCUC2 |
| network_name_str |
REDICUC - Repositorio CUC |
| repository_id_str |
|
| dc.title.spa.fl_str_mv |
Classification of mitochondrial network images associated with the study of breast cancer |
| title |
Classification of mitochondrial network images associated with the study of breast cancer |
| spellingShingle |
Classification of mitochondrial network images associated with the study of breast cancer Breast cáncer Classification of mitocondrial Network images |
| title_short |
Classification of mitochondrial network images associated with the study of breast cancer |
| title_full |
Classification of mitochondrial network images associated with the study of breast cancer |
| title_fullStr |
Classification of mitochondrial network images associated with the study of breast cancer |
| title_full_unstemmed |
Classification of mitochondrial network images associated with the study of breast cancer |
| title_sort |
Classification of mitochondrial network images associated with the study of breast cancer |
| dc.creator.fl_str_mv |
Silva, Jesús Varela Izquierdo, Noel Diaz Arroyo, Esperanza Pineda, Omar |
| dc.contributor.author.spa.fl_str_mv |
Silva, Jesús Varela Izquierdo, Noel Diaz Arroyo, Esperanza Pineda, Omar |
| dc.subject.spa.fl_str_mv |
Breast cáncer Classification of mitocondrial Network images |
| topic |
Breast cáncer Classification of mitocondrial Network images |
| description |
Within 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. |
| publishDate |
2020 |
| dc.date.accessioned.none.fl_str_mv |
2020-11-11T16:41:48Z |
| dc.date.available.none.fl_str_mv |
2020-11-11T16:41:48Z |
| dc.date.issued.none.fl_str_mv |
2020 |
| dc.date.embargoEnd.none.fl_str_mv |
2021-05-07 |
| dc.type.spa.fl_str_mv |
Pre-Publicación |
| dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_816b |
| dc.type.content.spa.fl_str_mv |
Text |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/preprint |
| dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/ARTOTR |
| dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
| format |
http://purl.org/coar/resource_type/c_816b |
| status_str |
acceptedVersion |
| dc.identifier.issn.spa.fl_str_mv |
2194-5357 |
| dc.identifier.uri.spa.fl_str_mv |
https://hdl.handle.net/11323/7255 |
| dc.identifier.instname.spa.fl_str_mv |
Corporación Universidad de la Costa |
| dc.identifier.reponame.spa.fl_str_mv |
REDICUC - Repositorio CUC |
| dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.cuc.edu.co/ |
| identifier_str_mv |
2194-5357 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
| url |
https://hdl.handle.net/11323/7255 https://repositorio.cuc.edu.co/ |
| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
| dc.relation.references.spa.fl_str_mv |
Fang, 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) Zhang, 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) Terao, 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) Caino, 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) Gonzalez, C.R., Woods, R.: Digital Image Processing, pp. 78–135. Prentice Hall, Upper Saddle River (2007) Bishop, C.M.: Pattern Recognition and Machine Learning, pp. 423–428. Springer, Heidelberg (2006) Iqbal, 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) Aggarwal, 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) Bindhu, V.: Biomedical image analysis using semantic segmentation. J. Innov. Image Process. (JIIP) 1(02), 91–101 (2019) Escala-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) Reis, 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) Hamacher-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) Wang, 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) v Darvishi, 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) Crudele, 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) Jin, 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) Vernier, 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) Yang, 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) Ezzati, M., Yousefi, B., Velaei, K., Safa, A.: A review on anti-cancer properties of Quercetin in breast cancer. Life Sci. 117463 (2020) Viloria, 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) Smolková, 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) Smolková, 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) Viloria, 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) Hannemann, 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) Dekker, 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) Giedt, 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) Shermis, 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) Varela, 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) Jones, 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) |
| dc.rights.spa.fl_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International |
| dc.rights.uri.spa.fl_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/closedAccess |
| dc.rights.coar.spa.fl_str_mv |
http://purl.org/coar/access_right/c_14cb |
| rights_invalid_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ http://purl.org/coar/access_right/c_14cb |
| eu_rights_str_mv |
closedAccess |
| dc.format.mimetype.spa.fl_str_mv |
application/pdf |
| dc.publisher.spa.fl_str_mv |
Corporación Universidad de la Costa |
| dc.source.spa.fl_str_mv |
Advances in Intelligent Systems and Computing |
| institution |
Corporación Universidad de la Costa |
| dc.source.url.spa.fl_str_mv |
https://www.scopus.com/record/display.uri?eid=2-s2.0-85089236108&doi=10.1007%2f978-3-030-51859-2_17&origin=inward&txGid=7f0968ee3e9c5c0311b8acf5d8e7b846 |
| bitstream.url.fl_str_mv |
https://repositorio.cuc.edu.co/bitstreams/6d66030b-d7f4-40d8-b77e-48fa5c1e3fcd/download https://repositorio.cuc.edu.co/bitstreams/82df78a0-b0c7-41f3-a497-88416359ad03/download https://repositorio.cuc.edu.co/bitstreams/053aa85b-6087-44ee-bac5-8495f986aa54/download https://repositorio.cuc.edu.co/bitstreams/325796aa-455e-44fe-b956-ab4e9c9efd28/download https://repositorio.cuc.edu.co/bitstreams/fbde4fa2-3f35-428a-a6af-c36032398893/download |
| bitstream.checksum.fl_str_mv |
6a371f2a6616e4766b9dd2291c18eac3 4460e5956bc1d1639be9ae6146a50347 e30e9215131d99561d40d6b0abbe9bad 8aef6d0df6c0e74485b87558fb9ed0f3 d2bdb083c440b32872fd760ba2fb5544 |
| bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 MD5 |
| repository.name.fl_str_mv |
Repositorio de la Universidad de la Costa CUC |
| repository.mail.fl_str_mv |
repdigital@cuc.edu.co |
| _version_ |
1828166819242835968 |
| spelling |
Silva, JesúsVarela Izquierdo, NoelDiaz Arroyo, EsperanzaPineda, Omar2020-11-11T16:41:48Z2020-11-11T16:41:48Z20202021-05-072194-5357https://hdl.handle.net/11323/7255Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Within 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.Silva, JesúsVarela Izquierdo, Noel-will be generated-orcid-0000-0001-7036-4414-600Diaz Arroyo, Esperanza-will be generated-orcid-0000-0002-3286-022X-600Pineda, Omar-will be generated-orcid-0000-0002-8239-3906-600application/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbAdvances in Intelligent Systems and Computinghttps://www.scopus.com/record/display.uri?eid=2-s2.0-85089236108&doi=10.1007%2f978-3-030-51859-2_17&origin=inward&txGid=7f0968ee3e9c5c0311b8acf5d8e7b846Breast cáncerClassification of mitocondrialNetwork imagesClassification of mitochondrial network images associated with the study of breast cancerPre-Publicaciónhttp://purl.org/coar/resource_type/c_816bTextinfo:eu-repo/semantics/preprinthttp://purl.org/redcol/resource_type/ARTOTRinfo:eu-repo/semantics/acceptedVersionFang, 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)Zhang, 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)Terao, 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)Caino, 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)Gonzalez, C.R., Woods, R.: Digital Image Processing, pp. 78–135. Prentice Hall, Upper Saddle River (2007)Bishop, C.M.: Pattern Recognition and Machine Learning, pp. 423–428. Springer, Heidelberg (2006)Iqbal, 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)Aggarwal, 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)Bindhu, V.: Biomedical image analysis using semantic segmentation. J. Innov. Image Process. (JIIP) 1(02), 91–101 (2019)Escala-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)Reis, 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)Hamacher-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)Wang, 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) vDarvishi, 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)Crudele, 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)Jin, 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)Vernier, 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)Yang, 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)Ezzati, M., Yousefi, B., Velaei, K., Safa, A.: A review on anti-cancer properties of Quercetin in breast cancer. Life Sci. 117463 (2020)Viloria, 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)Smolková, 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)Smolková, 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)Viloria, 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)Hannemann, 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)Dekker, 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)Giedt, 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)Shermis, 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)Varela, 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)Jones, 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)PublicationORIGINALCLASSIFICATION OF MITOCHONDRIAL NETWORK IMAGES ASSOCIATED WITH THE STUDY OF BREAST CÁNCER.pdfCLASSIFICATION OF MITOCHONDRIAL NETWORK IMAGES ASSOCIATED WITH THE STUDY OF BREAST CÁNCER.pdfapplication/pdf6412https://repositorio.cuc.edu.co/bitstreams/6d66030b-d7f4-40d8-b77e-48fa5c1e3fcd/download6a371f2a6616e4766b9dd2291c18eac3MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.cuc.edu.co/bitstreams/82df78a0-b0c7-41f3-a497-88416359ad03/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/053aa85b-6087-44ee-bac5-8495f986aa54/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILCLASSIFICATION OF MITOCHONDRIAL NETWORK IMAGES ASSOCIATED WITH THE STUDY OF BREAST CÁNCER.pdf.jpgCLASSIFICATION OF MITOCHONDRIAL NETWORK IMAGES ASSOCIATED WITH THE STUDY OF BREAST CÁNCER.pdf.jpgimage/jpeg40188https://repositorio.cuc.edu.co/bitstreams/325796aa-455e-44fe-b956-ab4e9c9efd28/download8aef6d0df6c0e74485b87558fb9ed0f3MD54TEXTCLASSIFICATION OF MITOCHONDRIAL NETWORK IMAGES ASSOCIATED WITH THE STUDY OF BREAST CÁNCER.pdf.txtCLASSIFICATION OF MITOCHONDRIAL NETWORK IMAGES ASSOCIATED WITH THE STUDY OF BREAST CÁNCER.pdf.txttext/plain1093https://repositorio.cuc.edu.co/bitstreams/fbde4fa2-3f35-428a-a6af-c36032398893/downloadd2bdb083c440b32872fd760ba2fb5544MD5511323/7255oai:repositorio.cuc.edu.co:11323/72552024-09-17 14:13:44.368http://creativecommons.org/licenses/by-nc-nd/4.0/Attribution-NonCommercial-NoDerivatives 4.0 Internationalopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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 |