Zobrazeno 1 - 10
of 34
pro vyhledávání: '"Eric Stahlberg"'
Autor:
Keyur Talsania, Tsai-wei Shen, Xiongfong Chen, Erich Jaeger, Zhipan Li, Zhong Chen, Wanqiu Chen, Bao Tran, Rebecca Kusko, Limin Wang, Andy Wing Chun Pang, Zhaowei Yang, Sulbha Choudhari, Michael Colgan, Li Tai Fang, Andrew Carroll, Jyoti Shetty, Yuliya Kriga, Oksana German, Tatyana Smirnova, Tiantain Liu, Jing Li, Ben Kellman, Karl Hong, Alex R. Hastie, Aparna Natarajan, Ali Moshrefi, Anastasiya Granat, Tiffany Truong, Robin Bombardi, Veronnica Mankinen, Daoud Meerzaman, Christopher E. Mason, Jack Collins, Eric Stahlberg, Chunlin Xiao, Charles Wang, Wenming Xiao, Yongmei Zhao
Publikováno v:
Genome Biology, Vol 23, Iss 1, Pp 1-33 (2022)
Abstract Background The cancer genome is commonly altered with thousands of structural rearrangements including insertions, deletions, translocation, inversions, duplications, and copy number variations. Thus, structural variant (SV) characterization
Externí odkaz:
https://doaj.org/article/3cca47d9f72844a39a3e5ac2ef139231
Autor:
Tanmoy Bhattacharya, Thomas Brettin, James H. Doroshow, Yvonne A. Evrard, Emily J. Greenspan, Amy L. Gryshuk, Thuc T. Hoang, Carolyn B. Vea Lauzon, Dwight Nissley, Lynne Penberthy, Eric Stahlberg, Rick Stevens, Fred Streitz, Georgia Tourassi, Fangfang Xia, George Zaki
Publikováno v:
Frontiers in Oncology, Vol 9 (2019)
The application of data science in cancer research has been boosted by major advances in three primary areas: (1) Data: diversity, amount, and availability of biomedical data; (2) Advances in Artificial Intelligence (AI) and Machine Learning (ML) alg
Externí odkaz:
https://doaj.org/article/9e82654f1abd4a87a245b6b2ae1fe724
Autor:
Sunita Chandrasekaran, Eric Stahlberg
Publikováno v:
BMC Bioinformatics, Vol 19, Iss S18, Pp 1-2 (2018)
Externí odkaz:
https://doaj.org/article/9cf1d5ca49cb4706a89b042cdd2fcc17
Autor:
Sunita Chandrasekaran, Eric Stahlberg
Publikováno v:
BMC Bioinformatics, Vol 20, Iss 1, Pp 1-1 (2019)
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Externí odkaz:
https://doaj.org/article/e5a4a5169e204d828abd18e26bcc287d
Autor:
Han Si, Paola Scaffidi, Anand Merchant, Maggie Cam, Eric Stahlberg, Tom Misteli, Patricia Fernandez
Publikováno v:
Genomics Data, Vol 3, Iss C, Pp 33-35 (2015)
Hutchinson–Gilford progeria syndrome (HGPS) patients do not develop cancer despite a significant accumulation of DNA damage in their cells. We have recently reported that HGPS cells are refractory to experimental oncogenic transformation and we ide
Externí odkaz:
https://doaj.org/article/9ec7ffad5c25415cb9a09e60fa0afdf1
Autor:
Yun Ji, Natalie Abrams, Wei Zhu, Eddie Salinas, Zhiya Yu, Douglas C Palmer, Parthav Jailwala, Zulmarie Franco, Rahul Roychoudhuri, Eric Stahlberg, Luca Gattinoni, Nicholas P Restifo
Publikováno v:
PLoS ONE, Vol 9, Iss 5, p e96650 (2014)
The pmel-1 T cell receptor transgenic mouse has been extensively employed as an ideal model system to study the mechanisms of tumor immunology, CD8+ T cell differentiation, autoimmunity and adoptive immunotherapy. The 'zygosity' of the transgene affe
Externí odkaz:
https://doaj.org/article/6ccd89370fba425082b0675bbff7a092
Autor:
Tanveer Syeda-Mahmood, Ilya Shmulevich, Emily J. Greenspan, Tina Hernandez-Boussard, Amy L. Gryshuk, Eric Stahlberg, Paul Macklin
Publikováno v:
Nature Medicine
Nat Med
Nat Med
Autor:
Pinyi Lu, Rick Stevens, Eric Stahlberg, Thomas Brettin, Maulik Shukla, Jason D. Gans, Alexander Partin, Justin M. Wozniak, Austin Clyde, Stewart He, Jonathan E. Allen, Hyunseung Yoo, Fangfang Xia, George Zaki, Prasanna Balaprakash, Yitan Zhu, Cristina Garcia-Cardona, Ya Ju Fan, Xiaotian Duan, Yvonne A. Evrard, Sergei Maslov, James H. Doroshow, Veronika Dubinkina, Judith D. Cohn
Publikováno v:
Briefings in Bioinformatics
To enable personalized cancer treatment, machine learning models have been developed to predict drug response as a function of tumor and drug features. However, most algorithm development efforts have relied on cross validation within a single study
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::898ad84a11ff7d5a7662b49a33d97557
Autor:
Thomas Müller, Scott R. Brozell, Gergely Gidofalvi, Spiridoula Matsika, Gary S. Kedziora, Felix Plasser, Anita Das, Hans Lischka, Dana Nachtigallová, Reed Nieman, Ron Shepard, Elizete Ventura, Russell M. Pitzer, Mayzza M. Araújo Do Nascimento, Markus Oppel, Silmar A. do Monte, Leticia González, Adelia J. A. Aquino, Lachlan T. Belcher, Eric Stahlberg, Zhiyong Zhang, Emily A. Carter, William L. Hase, Miklos Kertesz, Rene F. K. Spada, Carol A. Parish, Péter G. Szalay, F. Kossoski, Mario Barbatti, Jean Philippe Blaudeau, David R. Yarkony, Itamar Borges, Francisco B. C. Machado
Publikováno v:
Journal of Chemical Physics
Journal of Chemical Physics, American Institute of Physics, 2020, 152 (13), pp.134110. ⟨10.1063/1.5144267⟩
The journal of chemical physics 152(13), 134110-(2020). doi:10.1063/1.5144267
Journal of Chemical Physics, 2020, 152 (13), pp.134110. ⟨10.1063/1.5144267⟩
Journal of Chemical Physics, American Institute of Physics, 2020, 152 (13), pp.134110. ⟨10.1063/1.5144267⟩
The journal of chemical physics 152(13), 134110-(2020). doi:10.1063/1.5144267
Journal of Chemical Physics, 2020, 152 (13), pp.134110. ⟨10.1063/1.5144267⟩
International audience; The core part of the program system COLUMBUS allows highly efficient calculations using variational multireference (MR) methods in the framework of configuration interaction with single and double excitations (MR-CISD) and ave
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::49d23109b2920dc93d81e50f55523b4d
https://hal-amu.archives-ouvertes.fr/hal-02612344
https://hal-amu.archives-ouvertes.fr/hal-02612344
Autor:
Thuc T Hoang, Fangfang Xia, Dwight V. Nissley, Emily J. Greenspan, Thomas Brettin, Rick Stevens, Tanmoy Bhattacharya, James H. Doroshow, Eric Stahlberg, Georgia D. Tourassi, Yvonne A. Evrard, Frederick H. Streitz, Lynne Penberthy, Carolyn B Vea Lauzon, George Zaki, Amy L. Gryshuk
Publikováno v:
Frontiers in Oncology
Frontiers in Oncology, Vol 9 (2019)
Frontiers in Oncology, Vol 9 (2019)
The application of data science in cancer research has been boosted by major advances in three primary areas: (1) Data: diversity, amount, and availability of biomedical data; (2) Advances in Artificial Intelligence (AI) and Machine Learning (ML) alg