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Artigo de periodico
Machine learning identifies stemness features associated with oncogenic dedifferentiation (2018)
Malta, Tathiane M.; Noushmehr, Houtan; Carlotti Júnior, Carlos Gilberto; Santos, José Sebastião dos; Kemp, Rafael; Sankarankutty, Ajith Kumar; Tirapelli, Daniela Pretti da Cunha
Source: Cell. Unidade: FMRP
Subjects: GENÔMICA, NEOPLASIAS, CÉLULAS-TRONCO, APRENDIZADO COMPUTACIONAL, ALGORITMOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MALTA, Tathiane M. et al. Machine learning identifies stemness features associated with oncogenic dedifferentiation. Cell, v. 173, n. 2, p. 338-354.e1-e5, 2018Tradução . . Disponível em: https://doi.org/10.1016/j.cell.2018.03.034. Acesso em: 01 nov. 2024.
APA
Malta, T. M., Noushmehr, H., Carlotti Júnior, C. G., Santos, J. S. dos, Kemp, R., Sankarankutty, A. K., & Tirapelli, D. P. da C. (2018). Machine learning identifies stemness features associated with oncogenic dedifferentiation. Cell, 173( 2), 338-354.e1-e5. doi:10.1016/j.cell.2018.03.034
NLM
Malta TM, Noushmehr H, Carlotti Júnior CG, Santos JS dos, Kemp R, Sankarankutty AK, Tirapelli DP da C. Machine learning identifies stemness features associated with oncogenic dedifferentiation [Internet]. Cell. 2018 ; 173( 2): 338-354.e1-e5.[citado 2024 nov. 01 ] Available from: https://doi.org/10.1016/j.cell.2018.03.034
Vancouver
Malta TM, Noushmehr H, Carlotti Júnior CG, Santos JS dos, Kemp R, Sankarankutty AK, Tirapelli DP da C. Machine learning identifies stemness features associated with oncogenic dedifferentiation [Internet]. Cell. 2018 ; 173( 2): 338-354.e1-e5.[citado 2024 nov. 01 ] Available from: https://doi.org/10.1016/j.cell.2018.03.034
Artigo de periodico
Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images (2018)
Saltz, Joel; Gupta, Rajarsi; Hou, Le; Kurc, Tahsin; Singh, Pankaj; Nguyen, Vu; Samaras, Dimitris; Shroyer, Kenneth R.; Zhao, Tianhao; Batiste, Rebecca; Van Arnam, John; Shmulevich, Ilya; Rao, Arvind; Lazar, Alexander J.; Sharma, Ashish; Thorsson, Vésteinn; Noushmehr, Houtan; Carlotti Júnior, Carlos Gilberto; Santos, José Sebastião dos; Kemp, Rafael; Sankarankuty, Ajith Kumar; Tirapelli, Daniela Pretti da Cunha
Source: Cell Reports. Unidade: FMRP
Subjects: NEOPLASIAS, APRENDIZADO COMPUTACIONAL, INTELIGÊNCIA ARTIFICIAL, BIOINFORMÁTICA, LINFÓCITOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SALTZ, Joel et al. Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images. Cell Reports, v. 23, n. 1, p. 181-193, 2018Tradução . . Disponível em: https://doi.org/10.1016/j.celrep.2018.03.086. Acesso em: 01 nov. 2024.
APA
Saltz, J., Gupta, R., Hou, L., Kurc, T., Singh, P., Nguyen, V., et al. (2018). Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images. Cell Reports, 23( 1), 181-193. doi:10.1016/j.celrep.2018.03.086
NLM
Saltz J, Gupta R, Hou L, Kurc T, Singh P, Nguyen V, Samaras D, Shroyer KR, Zhao T, Batiste R, Van Arnam J, Shmulevich I, Rao A, Lazar AJ, Sharma A, Thorsson V, Noushmehr H, Carlotti Júnior CG, Santos JS dos, Kemp R, Sankarankuty AK, Tirapelli DP da C. Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images [Internet]. Cell Reports. 2018 ; 23( 1): 181-193.[citado 2024 nov. 01 ] Available from: https://doi.org/10.1016/j.celrep.2018.03.086
Vancouver
Saltz J, Gupta R, Hou L, Kurc T, Singh P, Nguyen V, Samaras D, Shroyer KR, Zhao T, Batiste R, Van Arnam J, Shmulevich I, Rao A, Lazar AJ, Sharma A, Thorsson V, Noushmehr H, Carlotti Júnior CG, Santos JS dos, Kemp R, Sankarankuty AK, Tirapelli DP da C. Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images [Internet]. Cell Reports. 2018 ; 23( 1): 181-193.[citado 2024 nov. 01 ] Available from: https://doi.org/10.1016/j.celrep.2018.03.086
Artigo de periodico
Machine learning detects pan-cancer ras pathway activation in the cancer genome atlas (2018)
Noushmehr, Houtan; Carlotti Júnior, Carlos Gilberto; Santos, José Sebastião dos; Kemp, Rafael; Sankarankutty, Ajith Kumar; Tirapelli, Daniela Pretti da Cunha
Source: Cell Reports. Unidade: FMRP
Subjects: NEOPLASIAS, GENÔMICA, APRENDIZADO COMPUTACIONAL, EXPRESSÃO GÊNICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
NOUSHMEHR, Houtan et al. Machine learning detects pan-cancer ras pathway activation in the cancer genome atlas. Cell Reports, v. 23, n. 1, p. 172-180, 2018Tradução . . Disponível em: https://doi.org/10.1016/j.celrep.2018.03.046. Acesso em: 01 nov. 2024.
APA
Noushmehr, H., Carlotti Júnior, C. G., Santos, J. S. dos, Kemp, R., Sankarankutty, A. K., & Tirapelli, D. P. da C. (2018). Machine learning detects pan-cancer ras pathway activation in the cancer genome atlas. Cell Reports, 23( 1), 172-180. doi:10.1016/j.celrep.2018.03.046
NLM
Noushmehr H, Carlotti Júnior CG, Santos JS dos, Kemp R, Sankarankutty AK, Tirapelli DP da C. Machine learning detects pan-cancer ras pathway activation in the cancer genome atlas [Internet]. Cell Reports. 2018 ; 23( 1): 172-180.[citado 2024 nov. 01 ] Available from: https://doi.org/10.1016/j.celrep.2018.03.046
Vancouver
Noushmehr H, Carlotti Júnior CG, Santos JS dos, Kemp R, Sankarankutty AK, Tirapelli DP da C. Machine learning detects pan-cancer ras pathway activation in the cancer genome atlas [Internet]. Cell Reports. 2018 ; 23( 1): 172-180.[citado 2024 nov. 01 ] Available from: https://doi.org/10.1016/j.celrep.2018.03.046

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