Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Safiye Celik"'
Autor:
Joseph D. Janizek, Anna Spiro, Safiye Celik, Ben W. Blue, John C. Russell, Ting-I Lee, Matt Kaeberlin, Su-In Lee
Publikováno v:
Genome Biology, Vol 24, Iss 1, Pp 1-30 (2023)
Abstract As interest in using unsupervised deep learning models to analyze gene expression data has grown, an increasing number of methods have been developed to make these models more interpretable. These methods can be separated into two groups: po
Externí odkaz:
https://doaj.org/article/e28d031d40c24e3483d38d0e92b91ca7
Autor:
Nicasia Beebe-Wang, Safiye Celik, Ethan Weinberger, Pascal Sturmfels, Philip L. De Jager, Sara Mostafavi, Su-In Lee
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-17 (2021)
The molecular basis of Alzheimer’s Disease has been obscured by heterogeneity and scarcity of brain gene expression data, which limit effectiveness in complex models. Here, the authors introduce a multi-task deep learning framework to learn general
Externí odkaz:
https://doaj.org/article/d78ddb707ec94c1ab887dc583c8632d4
Autor:
Su-In Lee, Safiye Celik, Benjamin A. Logsdon, Scott M. Lundberg, Timothy J. Martins, Vivian G. Oehler, Elihu H. Estey, Chris P. Miller, Sylvia Chien, Jin Dai, Akanksha Saxena, C. Anthony Blau, Pamela S. Becker
Publikováno v:
Nature Communications, Vol 9, Iss 1, Pp 1-13 (2018)
Identification of markers of drug response is essential for precision therapy. Here the authors introduce an algorithm that uses prior information about each gene’s importance in AML to identify the most predictive gene-drug associations from trans
Externí odkaz:
https://doaj.org/article/ff277a1927e34d3491a2ec37c88816b8
Autor:
Joseph D. Janizek, Ayse B. Dincer, Safiye Celik, Hugh Chen, William Chen, Kamila Naxerova, Su-In Lee
Publikováno v:
Nature Biomedical Engineering.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3a813dd8d80a3137c8069913b3ebf96b
https://doi.org/10.1038/s41551-023-01034-0
https://doi.org/10.1038/s41551-023-01034-0
Autor:
Nathan H. Lazar, Safiye Celik, Lu Chen, Marta Fay, Jonathan C. Irish, James Jensen, Conor A. Tillinghast, John Urbanik, William P. Bone, Genevieve H. L. Roberts, Christopher C. Gibson, Imran S. Haque
SummaryCRISPR-Cas9 editing is a scalable technology for mapping of biological pathways, but it has been reported to cause a variety of undesired large-scale structural changes to the genome. We performed an arrayed CRISPR-Cas9 scan of the genome in p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::13a0dbf22269b50e5805eafaa6f2dcda
https://doi.org/10.1101/2023.04.15.537038
https://doi.org/10.1101/2023.04.15.537038
Autor:
Marta M. Fay, Oren Kraus, Mason Victors, Lakshmanan Arumugam, Kamal Vuggumudi, John Urbanik, Kyle Hansen, Safiye Celik, Nico Cernek, Ganesh Jagannathan, Jordan Christensen, Berton A. Earnshaw, Imran S. Haque, Ben Mabey
The combination of modern genetic perturbation techniques with high content screening has enabled genome-scale cell microscopy experiments that can be leveraged to constructmaps of biology. These are built by processing microscopy images to produce r
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4b35ff2d1c660bd20c9a37b2f3db115b
https://doi.org/10.1101/2023.02.07.527350
https://doi.org/10.1101/2023.02.07.527350
Autor:
Safiye Celik, Jan-Christian Hütter, Sandra Melo Carlos, Nathan H Lazar, Rahul Mohan, Conor Tillinghast, Tommaso Biancalani, Marta Fay, Berton A Earnshaw, Imran S Haque
The continued scaling of genetic perturbation technologies combined with high-dimensional assays (microscopy and RNA-sequencing) has enabled genome-scale reverse-genetics experiments that go beyond single-endpoint measurements of growth or lethality.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::456229671e628931109c0f1826778fcb
https://doi.org/10.1101/2022.12.09.519400
https://doi.org/10.1101/2022.12.09.519400
Autor:
Joseph D. Janizek, Anna Spiro, Safiye Celik, Ben W. Blue, Josh C. Russell, Ting-I Lee, Matt Kaeberlin, Su-In Lee
As interest in unsupervised deep learning models for the analysis of gene expression data has grown, an increasing number of methods have been developed to make these deep learning models more interpretable. These methods can be separated into two gr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3450547f1c9a66466ca1c9fe69766bf2
https://doi.org/10.1101/2022.05.03.490535
https://doi.org/10.1101/2022.05.03.490535
Autor:
Safiye Celik, William Chen, Kamila Naxerova, Su-In Lee, Joseph D. Janizek, Hugh Chen, Ayse B. Dincer
Complex machine learning models are poised to revolutionize the treatment of diseases like acute myeloid leukemia (AML) by helping physicians choose optimal combinations of anti-cancer drugs based on molecular features. While accurate predictions are
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4e89bb60a6ebc0b7e809582decbb869b
https://doi.org/10.1101/2021.10.06.463409
https://doi.org/10.1101/2021.10.06.463409
Autor:
Pascal Sturmfels, Nicasia Beebe-Wang, Ethan Weinberger, Sara Mostafavi, Safiye Celik, Su-In Lee, Phillip L. De Jager
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-17 (2021)
Deep neural networks offer a promising approach for capturing complex, non-linear relationships among variables. Because they require immense sample sizes, their potential has yet to be fully tapped for understanding complex relationships between gen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::26bc77153ef9159958c66e1c5cbe9fc4
https://doi.org/10.1101/2020.11.30.404087
https://doi.org/10.1101/2020.11.30.404087