Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Arno van Hilten"'
Autor:
Arno van Hilten, Jeroen van Rooij, BIOS consortium, M. Arfan Ikram, Wiro J. Niessen, Joyce. B. J. van Meurs, Gennady V. Roshchupkin
Publikováno v:
npj Systems Biology and Applications, Vol 10, Iss 1, Pp 1-10 (2024)
Abstract Integrating multi-omics data into predictive models has the potential to enhance accuracy, which is essential for precision medicine. In this study, we developed interpretable predictive models for multi-omics data by employing neural networ
Externí odkaz:
https://doaj.org/article/296197b7ce774b4fbd8e3012971c3fd4
Autor:
Arno van Hilten, Steven A. Kushner, Manfred Kayser, M. Arfan Ikram, Hieab H. H. Adams, Caroline C. W. Klaver, Wiro J. Niessen, Gennady V. Roshchupkin
Publikováno v:
Communications Biology, Vol 4, Iss 1, Pp 1-9 (2021)
van Hilten and colleagues present GenNet, a deep-learning framework for predicting phenotype from genetic data. This framework generates interpretable neural networks that provide insight into the genetic basis of complex traits and diseases.
Externí odkaz:
https://doaj.org/article/204c0fb0a15742cb9761a666d57de397
Autor:
Mohsen Ghanbari, Rima Mustafa, Michelle Mens, Arno van Hilten, Jian Huang, Gennady Roshchupkin, Tianxiao Huan, Linda Broer, Paul Elliott, Daniel Levy, M. Arfan Ikram, Marina Evangelou, Abbas Dehghan
MicroRNAs (miRNAs) are small non-coding RNAs that post-transcriptionally regulate gene expression. Identification of genetic variants influencing the transcription of miRNAs can provide an understanding of their genetic regulation and implication in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3f9fa3c820dd270826fb3c5d9a52aa49
https://doi.org/10.21203/rs.3.rs-2155107/v1
https://doi.org/10.21203/rs.3.rs-2155107/v1
Autor:
Hieab H.H. Adams, Caroline C W Klaver, M. Arfan Ikram, Wiro J. Niessen, Gennady V. Roshchupkin, Steven A. Kushner, Manfred Kayser, Arno van Hilten
Deep learning is rarely used in population genomics because of the computational burden and challenges in interpreting neural networks. Here, we propose GenNet, a novel open-source deep learning framework for predicting phenotypes from genetic varian
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::825f914b8f215777a87abf2c415fdb95
https://doi.org/10.1101/2020.06.19.159152
https://doi.org/10.1101/2020.06.19.159152
Autor:
Silas Nyboe Ørting, Veronika Cheplygina, Matthias Hirth, Arno van Hilten, Panagiotis Mavridis, Christopher R. Madan, Oana Inel, Andrew Doyle, Helen Spiers
Publikováno v:
arXiv, 2019:1902.09159. Cornell University Library
Ørting, S, Doyle, A, Hilten, A V, Hirth, M, Inel, O, Madan, C R, Mavridis, P, Spiers, H & Cheplygina, V 2019, ' A Survey of Crowdsourcing in Medical Image Analysis ', arXiv . < http://arxiv.org/pdf/1902.09159v2 >
Human Computation Journal
Ørting, S N, Doyle, A, Hilten, A V, Hirth, M, Inel, O, Madan, C R, Mavridis, P, Spiers, H & Cheplygina, V 2020, ' A Survey of Crowdsourcing in Medical Image Analysis ', Human Computation, vol. 7 . https://doi.org/10.15346/hc.v7i1.111
Ørting, S, Doyle, A, Hilten, A V, Hirth, M, Inel, O, Madan, C R, Mavridis, P, Spiers, H & Cheplygina, V 2019, ' A Survey of Crowdsourcing in Medical Image Analysis ', arXiv . < http://arxiv.org/pdf/1902.09159v2 >
Human Computation Journal
Ørting, S N, Doyle, A, Hilten, A V, Hirth, M, Inel, O, Madan, C R, Mavridis, P, Spiers, H & Cheplygina, V 2020, ' A Survey of Crowdsourcing in Medical Image Analysis ', Human Computation, vol. 7 . https://doi.org/10.15346/hc.v7i1.111
Rapid advances in image processing capabilities have been seen across many domains, fostered by the application of machine learning algorithms to "big-data". However, within the realm of medical image analysis, advances have been curtailed, in part,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0aa42073b82f4ab4afdb0032dca028e9
https://doi.org/10.48550/arxiv.1902.09159
https://doi.org/10.48550/arxiv.1902.09159
Publikováno v:
European Neuropsychopharmacology. 29:S263