Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Prashnna K. Gyawali"'
Autor:
Chowdhury Mohammad Abid Rahman, Ghadendra Bhandari, Nasser M. Nasrabadi, Aldo H. Romero, Prashnna K. Gyawali
Publikováno v:
Frontiers in Materials, Vol 11 (2024)
Machine learning (ML) models have emerged as powerful tools for accelerating materials discovery and design by enabling accurate predictions of properties from compositional and structural data. These capabilities are vital for developing advanced te
Externí odkaz:
https://doaj.org/article/769efd3b70da4da6a1005e3b76b1c0b5
Autor:
Prashnna K. Gyawali, Yann Le Guen, Xiaoxia Liu, Michael E. Belloy, Hua Tang, James Zou, Zihuai He
Publikováno v:
Communications Biology, Vol 6, Iss 1, Pp 1-9 (2023)
Abstract Risk prediction models using genetic data have seen increasing traction in genomics. However, most of the polygenic risk models were developed using data from participants with similar (mostly European) ancestry. This can lead to biases in t
Externí odkaz:
https://doaj.org/article/7ee2b42c6f4544aaaf46e766147b02dd
Autor:
Zihuai He, Linxi Liu, Michael E. Belloy, Yann Le Guen, Aaron Sossin, Xiaoxia Liu, Xinran Qi, Shiyang Ma, Prashnna K. Gyawali, Tony Wyss-Coray, Hua Tang, Chiara Sabatti, Emmanuel Candès, Michael D. Greicius, Iuliana Ionita-Laza
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-16 (2022)
The authors present GhostKnockoff, a method for genome-wide association studies which can be applied to enhance existing and future studies to identify functional variants with weaker statistical effects that might be missed by conventional associati
Externí odkaz:
https://doaj.org/article/8a4affe73e044277a90ea1ba3e8812dc
Autor:
Maryam Toloubidokhti, Nilesh Kumar, Zhiyuan Li, Prashnna K. Gyawali, Brian Zenger, Wilson W. Good, Rob S. MacLeod, Linwei Wang
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031164514
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d933ff80a13b5956e9f871e58d294c7b
https://doi.org/10.1007/978-3-031-16452-1_44
https://doi.org/10.1007/978-3-031-16452-1_44
