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Predicting protein condensate formation using machine learning
Predicting protein condensate formation using machine learning
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Predicting protein condensate formation using machine learning

van Mierlo, Guido ; Jansen, Jurriaan R.G. ; Wang, Jie ; Poser, Ina ; van Heeringen, Simon J. ; Vermeulen, Michiel

Cell reports (Cambridge), 2021-02, Vol.34 (5), p.108705-108705, Article 108705 [Periódico revisado por pares]

United States: Elsevier Inc

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2
Machine learning detects pan-cancer ras pathway activation in the cancer genome atlas
Machine learning detects pan-cancer ras pathway activation in the cancer genome atlas
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Machine learning detects pan-cancer ras pathway activation in the cancer genome atlas

Houtan Noushmehr Carlos Gilberto Carlotti Júnior; José Sebastião dos Santos; Rafael Kemp; Ajith Kumar Sankarankutty; Daniela Pretti da Cunha Tirapelli

Cell Reports Cambridge v. 23, n. 1, p. 172-180, 2018

Cambridge 2018

Localização: FMRP - Fac. Medicina de Ribeirão Preto    (pcd 2938459 Estantes Deslizantes )(Acessar)

3
Machine-Learning-Based Analysis in Genome-Edited Cells Reveals the Efficiency of Clathrin-Mediated Endocytosis
Machine-Learning-Based Analysis in Genome-Edited Cells Reveals the Efficiency of Clathrin-Mediated Endocytosis
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Machine-Learning-Based Analysis in Genome-Edited Cells Reveals the Efficiency of Clathrin-Mediated Endocytosis

Hong, Sun Hae ; Cortesio, Christa L. ; Drubin, David G.

Cell reports (Cambridge), 2015-09, Vol.12 (12), p.2121-2130 [Periódico revisado por pares]

United States: Elsevier Inc

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4
Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images
Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images
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Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images

Joel Saltz Rajarsi Gupta; Le Hou; Tahsin Kurc; Pankaj Singh; Vu Nguyen; Dimitris Samaras; Kenneth R Shroyer; Tianhao Zhao; Rebecca Batiste; John Van Arnam; Ilya Shmulevich; Arvind Rao; Alexander J Lazar; Ashish Sharma; Vésteinn Thorsson; Houtan Noushmehr; Carlos Gilberto Carlotti Júnior; José Sebastião dos Santos; Rafael Kemp; Ajith Kumar Sankarankuty; Daniela Pretti da Cunha Tirapelli; The Cancer Genome Atlas Research Network

Cell Reports Cambridge v. 23, n. 1, p. 181-193, 2018

Cambridge 2018

Localização: FMRP - Fac. Medicina de Ribeirão Preto    (pcd 2938421 Estantes Deslizantes )(Acessar)

5
A study of high-grade serous ovarian cancer origins implicates the SOX18 transcription factor in tumor development
A study of high-grade serous ovarian cancer origins implicates the SOX18 transcription factor in tumor development
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A study of high-grade serous ovarian cancer origins implicates the SOX18 transcription factor in tumor development

Kate Lawrenson Marcos A. S Fonseca; Annie Y Liu; Felipe Segato Dezem; Janet M Lee; Xianzhi Lin; Rosario I Corona; Forough Abbasi; Kevin C Vavra; Huy Q Dinh; Navjot Kaur Gill; Houtan Noushmehr

Cell Reports Cambridge v. 29, n. 11, p. 3726-3735.e1-e4, 2019

Cambridge 2019

Localização: FMRP - Fac. Medicina de Ribeirão Preto    (pcd 3026806 Acervo Digital )(Acessar)

6
Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas
Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas
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Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas

Mina, Marco ; Demchok, John A. ; Ferguson, Martin L. ; Tarnuzzer, Roy ; Yang, Liming ; DeFreitas, Timothy ; Noble, Michael S. ; Voet, Doug ; Dhankani, Varsha ; Leinonen, Kalle ; Ju, Zhenlin ; Korkut, Anil ; Liu, Wenbin ; Weinstein, John N. ; Abeshouse, Adam ; Chakravarty, Debyani ; Taylor, Barry S. ; Anur, Pavana ; Wong, Christopher K. ; Yau, Christina ; Ally, Adrian ; Kasaian, Katayoon ; Mayo, Michael ; Moore, Richard A. ; Berger, Ashton C. ; Gao, Galen F. ; Meyerson, Matthew ; Danilova, Ludmila ; Bootwalla, Moiz S. ; Hoadley, Katherine A. ; Jones, Corbin D. ; Mose, Lisle E. ; Tan, Donghui ; Hu, Jianhong ; Liu, Xiuping ; Sheth, Margi ; Bailey, Matthew ; Fronick, Catrina C. ; Kandoth, Cyriac ; Wilson, Richard K. ; Lau, Kevin ; Penny, Robert ; Thompson, Eric ; Fregnani, José H. ; McPherson, Christopher ; Berkowitz, Ross ; Barnholtz-Sloan, Jill S. ; Myers, Jerome ; Tucker, Kelinda ; Zelinka, Tomas ; Sepulveda, Antonia ; Desjardins, Laurence ; Lipp, Eric ; Sharp, Alexis ; Delman, Keith ; Owonikoko, Taofeek ; Oosterhuis, Wolter ; DiMeco, Francesco ; Finocchiaro, Gaetano ; Perin, Alessandro ; Wiznerowicz, Maciej ; Baudin, Eric ; Noushmehr, Houtan ; Scarpace, Lisa ; Dottino, Peter R. ; Breggia, Anne ; Bryce, Alan H. ; Copland, John A. ; Kocher, Jean-Pierre ; Petersen, Gloria ; Rivera, Michael ; Esmaeli, Bita ; Logothetis, Christopher ; Sood, Anil ; Kefford, Richard ; Shannon, Kerwin ; Ghossein, Ronald ; Smith, Jennifer ; Wentzensen, Nicolas ; Tcaciuc, Diana ; Engel, Jay ; Rassl, Doris M. ; Urba, Walter ; Merola, Roberta ; Cardinale, Vincenzo ; Carpino, Guido ; Cottingham, Sandra ; Janssen, Klaus-Peter ; Davis, Amy ; Lang, James ; Rai, Karan ; Kastl, Alison ; Waldmann, Jens ; Simon, Ronald ; Simko, Jeffry ; VandenBerg, Scott ; Unterberg, Andreas ; Beer, David ; Peterson, Lisa ; Ley, Timothy

Cell reports (Cambridge), 2018-04, Vol.23 (1), p.172-180.e3 [Periódico revisado por pares]

United States: Elsevier Inc

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7
Machine Learning Uncovers Food- and Excipient-Drug Interactions
Machine Learning Uncovers Food- and Excipient-Drug Interactions
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Machine Learning Uncovers Food- and Excipient-Drug Interactions

Reker, Daniel ; Shi, Yunhua ; Kirtane, Ameya R. ; Hess, Kaitlyn ; Zhong, Grace J. ; Crane, Evan ; Lin, Chih-Hsin ; Langer, Robert ; Traverso, Giovanni

Cell reports (Cambridge), 2020-03, Vol.30 (11), p.3710-3716.e4 [Periódico revisado por pares]

United States: Elsevier Inc

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8
Machine learning-based prediction models to guide the selection of Cas9 variants for efficient gene editing
Machine learning-based prediction models to guide the selection of Cas9 variants for efficient gene editing
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Machine learning-based prediction models to guide the selection of Cas9 variants for efficient gene editing

Li, Jianbo ; Wu, Panfeng ; Cao, Zhoutao ; Huang, Guanlan ; Lu, Zhike ; Yan, Jianfeng ; Zhang, Heng ; Zhou, Yangfan ; Liu, Rong ; Chen, Hui ; Ma, Lijia ; Luo, Mengcheng

Cell reports (Cambridge), 2024-02, Vol.43 (2), p.113765-113765, Article 113765 [Periódico revisado por pares]

United States: Elsevier Inc

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9
Deep neural networks identify signaling mechanisms of ErbB-family drug resistance from a continuous cell morphology space
Deep neural networks identify signaling mechanisms of ErbB-family drug resistance from a continuous cell morphology space
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Deep neural networks identify signaling mechanisms of ErbB-family drug resistance from a continuous cell morphology space

Longden, James ; Robin, Xavier ; Engel, Mathias ; Ferkinghoff-Borg, Jesper ; Kjær, Ida ; Horak, Ivan D. ; Pedersen, Mikkel W. ; Linding, Rune

Cell reports (Cambridge), 2021-01, Vol.34 (3), p.108657-108657, Article 108657 [Periódico revisado por pares]

United States: Elsevier Inc

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10
Predicting protein interaction network perturbation by alternative splicing with semi-supervised learning
Predicting protein interaction network perturbation by alternative splicing with semi-supervised learning
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Predicting protein interaction network perturbation by alternative splicing with semi-supervised learning

Narykov, Oleksandr ; Johnson, Nathan T. ; Korkin, Dmitry

Cell reports (Cambridge), 2021-11, Vol.37 (8), p.110045-110045, Article 110045 [Periódico revisado por pares]

United States: Elsevier Inc

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Deste Autor:

  1. Noushmehr, H
  2. Santos, J
  3. Carlotti Júnior, C
  4. Kemp, R
  5. Tirapelli, D

Neste Assunto:

  1. Life Sciences & Biomedicine
  2. Science & Technology
  3. Cell Biology
  4. Machine Learning
  5. Humans

  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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