Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Joseph D Janizek"'
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:
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
Publikováno v:
Nature Machine Intelligence. 3:620-631
Recent research has demonstrated that feature attribution methods for deep networks can themselves be incorporated into training; these attribution priors optimize for a model whose attributions have certain desirable properties—most frequently, th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::28d48bbfa5fe1bec07ea38e2573e7558
https://doi.org/10.1038/s42256-021-00343-w
https://doi.org/10.1038/s42256-021-00343-w
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:
Joseph D. Janizek, Carly Hudelson, Richard B. Utarnachitt, Nathan J. White, Andrew M. McCoy, Gabriel G. Erion, Michael R. Sayre, Su-In Lee
The recent emergence of accurate artificial intelligence (AI) models for disease diagnosis raises the possibility that AI-based clinical decision support could substantially lower the workload of healthcare providers. However, for this to occur, the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f72aca75ab340541323ecf4400358510
https://doi.org/10.1101/2021.01.19.21249356
https://doi.org/10.1101/2021.01.19.21249356
Autor:
Gabriel, Erion, Joseph D, Janizek, Carly, Hudelson, Richard B, Utarnachitt, Andrew M, McCoy, Michael R, Sayre, Nathan J, White, Su-In, Lee
Publikováno v:
Nature biomedical engineering. 6(12)
Accurate artificial intelligence (AI) for disease diagnosis could lower healthcare workloads. However, when time or financial resources for gathering input data are limited, as in emergency and critical-care medicine, developing accurate AI models, w
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::f4e8d5e89f7e6534fa0feb95932caf61
https://pubmed.ncbi.nlm.nih.gov/35393566
https://pubmed.ncbi.nlm.nih.gov/35393566
Publikováno v:
medRxiv
article-version (status) pre
article-version (number) 1
article-version (status) pre
article-version (number) 1
Artificial intelligence (AI) researchers and radiologists have recently reported AI systems that accurately detect COVID-19 in chest radiographs. However, the robustness of these systems remains unclear. Using state-of-the-art techniques in explainab
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::045c88fbb94325166da3bba001a9c04b
https://doi.org/10.1101/2020.09.13.20193565
https://doi.org/10.1101/2020.09.13.20193565
Publikováno v:
Bioinformatics
Motivation Increasing number of gene expression profiles has enabled the use of complex models, such as deep unsupervised neural networks, to extract a latent space from these profiles. However, expression profiles, especially when collected in large
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::db64bcdb90faf292a6bad6ac1538c81c