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pro vyhledávání: '"QUACKENBUSH, JOHN"'
The practical utility of machine learning models in the sciences often hinges on their interpretability. It is common to assess a model's merit for scientific discovery, and thus novel insights, by how well it aligns with already available domain kno
Externí odkaz:
http://arxiv.org/abs/2403.04805
Modeling the time evolution of discrete sets of items (e.g., genetic mutations) is a fundamental problem in many biomedical applications. We approach this problem through the lens of continuous-time Markov chains, and show that the resulting learning
Externí odkaz:
http://arxiv.org/abs/2107.02911
Autor:
Shutta, Katherine H., Weighill, Deborah, Burkholz, Rebekka, Guebila, Marouen Ben, DeMeo, Dawn L., Zacharias, Helena U., Quackenbush, John, Altenbuchinger, Michael
Publikováno v:
Nucleic Acids Research, 51(3), e15-e15, 2022
The increasing quantity of multi-omics data, such as methylomic and transcriptomic profiles, collected on the same specimen, or even on the same cell, provide a unique opportunity to explore the complex interactions that define cell phenotype and gov
Externí odkaz:
http://arxiv.org/abs/2104.01690
Autor:
Weighill, Deborah, Guebila, Marouen Ben, Glass, Kimberly, Platig, John, Yeh, Jen Jen, Quackenbush, John
Profiling of whole transcriptomes has become a cornerstone of molecular biology and an invaluable tool for the characterization of clinical phenotypes and the identification of disease subtypes. Analyses of these data are becoming ever more sophistic
Externí odkaz:
http://arxiv.org/abs/2101.03985
Autor:
Haibe-Kains, Benjamin, Adam, George Alexandru, Hosny, Ahmed, Khodakarami, Farnoosh, Board, MAQC Society, Waldron, Levi, Wang, Bo, McIntosh, Chris, Kundaje, Anshul, Greene, Casey S., Hoffman, Michael M., Leek, Jeffrey T., Huber, Wolfgang, Brazma, Alvis, Pineau, Joelle, Tibshirani, Robert, Hastie, Trevor, Ioannidis, John P. A., Quackenbush, John, Aerts, Hugo J. W. L.
Publikováno v:
Nature 586 (2020) E14-E16
In their study, McKinney et al. showed the high potential of artificial intelligence for breast cancer screening. However, the lack of detailed methods and computer code undermines its scientific value. We identify obstacles hindering transparent and
Externí odkaz:
http://arxiv.org/abs/2003.00898
Akademický článek
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Autor:
Burkholz, Rebekka, Quackenbush, John
Cascade models are central to understanding, predicting, and controlling epidemic spreading and information propagation. Related optimization, including influence maximization, model parameter inference, or the development of vaccination strategies,
Externí odkaz:
http://arxiv.org/abs/1909.05416
Akademický článek
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Publikováno v:
In Cell Reports Methods 23 May 2022 2(5)
While we once thought of cancer as single monolithic diseases affecting a specific organ site, we now understand that there are many subtypes of cancer defined by unique patterns of gene mutations. These gene mutational data, which can be more reliab
Externí odkaz:
http://arxiv.org/abs/1703.01900