Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Kevin Faust"'
Autor:
K. H. Brian Lam, Alberto J. Leon, Weili Hui, Sandy Che-Eun Lee, Ihor Batruch, Kevin Faust, Almos Klekner, Gábor Hutóczki, Marianne Koritzinsky, Maxime Richer, Ugljesa Djuric, Phedias Diamandis
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-14 (2022)
Gioblastoma tumours consist of different niches defined by histology. Here, the authors use proteomics and machine learning to assign protein expression programs to these niches, and reveal that KRAS and hypoxia are associated with drug resistance.
Externí odkaz:
https://doaj.org/article/247ebc3f13744c4b98c1b4e41d550bef
Autor:
Kevin Faust, Quin Xie, Dominick Han, Kartikay Goyle, Zoya Volynskaya, Ugljesa Djuric, Phedias Diamandis
Publikováno v:
BMC Bioinformatics, Vol 19, Iss 1, Pp 1-15 (2018)
Abstract Background There is growing interest in utilizing artificial intelligence, and particularly deep learning, for computer vision in histopathology. While accumulating studies highlight expert-level performance of convolutional neural networks
Externí odkaz:
https://doaj.org/article/f5f4fb3cc349454babd8fe1cd8fb584f
Autor:
Anglin Dent, Kevin Faust, K. H. Brian Lam, Narges Alhangari, Alberto J. Leon, Queenie Tsang, Zaid Saeed Kamil, Andrew Gao, Prodipto Pal, Stephanie Lheureux, Amit Oza, Phedias Diamandis
SummaryIntra-tumoral heterogeneity can wreak havoc on current precision medicine strategies due to challenges in sufficient sampling of geographically separated areas of biodiversity distributed across centimeter-scale tumor distances. In particular,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d314eaa14b4dca7dafb6a85b8085f25a
https://doi.org/10.1101/2023.01.11.22283903
https://doi.org/10.1101/2023.01.11.22283903
Publikováno v:
ProteomicsREFERENCES. 22(23-24)
The human brain represents one of the most complex biological structures with significant spatiotemporal molecular plasticity occurring through early development, learning, aging, and disease. While much progress has been made in mapping its transcri
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::208d4c30fbe3d25e76a52223213172c0
https://pubmed.ncbi.nlm.nih.gov/35971647
https://pubmed.ncbi.nlm.nih.gov/35971647
Publikováno v:
Surgical Pathology Clinics. 13:349-358
Applications of artificial intelligence and particularly deep learning to aid pathologists in carrying out laborious and qualitative tasks in histopathologic image analysis have now become ubiquitous. We introduce and illustrate how unsupervised mach
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0de998965a29e52222ec71da44fc4911
https://doi.org/10.1016/j.path.2020.01.002
https://doi.org/10.1016/j.path.2020.01.002
Autor:
Kevin Faust, Michael K Lee, Anglin Dent, Clare Fiala, Alessia Portante, Madhumitha Rabindranath, Noor Alsafwani, Andrew Gao, Ugljesa Djuric, Phedias Diamandis
Publikováno v:
Neuro-Oncology Advances. 4
Background Modern molecular pathology workflows in neuro-oncology heavily rely on the integration of morphologic and immunohistochemical patterns for analysis, classification, and prognostication. However, despite the recent emergence of digital path
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c4685319d02d443ee29f5dac53f8c3a4
https://doi.org/10.1093/noajnl/vdac001
https://doi.org/10.1093/noajnl/vdac001
Autor:
Uglijesa Djuric, Raniah Al Qawahmed, Randy Van Ommeren, Kevin Faust, Phedias Diamandis, Sudarshan Bala, Alessia Portante
Publikováno v:
Nature Machine Intelligence. 1:316-321
Deep learning is an emerging transformative tool in diagnostic medicine, yet limited access and the interpretability of learned parameters hinders widespread adoption. Here we have generated a diverse repository of 838,644 histopathologic images and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::15564aee8c6854c237f1ecbf7e0fd2fc
https://doi.org/10.1038/s42256-019-0068-6
https://doi.org/10.1038/s42256-019-0068-6
Autor:
Ugljesa Djuric, Shihab Sarwar, Phedias Diamandis, Maxime Richer, Anglin Dent, Randy Van Ommeren, Kevin Faust
Publikováno v:
npj Digital Medicine, Vol 2, Iss 1, Pp 1-7 (2019)
NPJ Digital Medicine
NPJ Digital Medicine
Advancements in computer vision and artificial intelligence (AI) carry the potential to make significant contributions to health care, particularly in diagnostic specialties such as radiology and pathology. The impact of these technologies on physici
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3b91983903a1ccb312537a12cd3c8c58
http://link.springer.com/article/10.1038/s41746-019-0106-0
http://link.springer.com/article/10.1038/s41746-019-0106-0
Publikováno v:
Critical Reviews in Clinical Laboratory Sciences. 56:61-73
The precision-based revolution in medicine continues to demand stratification of patients into smaller and more personalized subgroups. While genomic technologies have largely led this movement, diagnostic results can take days to weeks to generate.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ed6d365bc8ef6812e5f14636d961d557
https://doi.org/10.1080/10408363.2018.1536111
https://doi.org/10.1080/10408363.2018.1536111
Publikováno v:
Cancer Research. 82:1685-1685
BACKGROUND: Emerging evidence strongly implicates intra-tumoral heterogeneous biology in treatment resistance and disease progression across many cancer types. Thus, there is a need for workflows capable of systematically resolving and targeting dist
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0dbbd3c8729c991822d0419a8df88260
https://doi.org/10.1158/1538-7445.am2022-1685
https://doi.org/10.1158/1538-7445.am2022-1685