Zobrazeno 1 - 10
of 268
pro vyhledávání: '"Jiann Shiun Yuan"'
Publikováno v:
BMC Bioinformatics, Vol 23, Iss 1, Pp 1-17 (2022)
Abstract Background Deep learning’s automatic feature extraction has proven to give superior performance in many sequence classification tasks. However, deep learning models generally require a massive amount of data to train, which in the case of
Externí odkaz:
https://doaj.org/article/604701393cb2494aa8c8d2de130071e3
Publikováno v:
Journal of Cheminformatics, Vol 14, Iss 1, Pp 1-18 (2022)
Abstract Deep learning’s automatic feature extraction has been a revolutionary addition to computational drug discovery, infusing both the capabilities of learning abstract features and discovering complex molecular patterns via learning from molec
Externí odkaz:
https://doaj.org/article/f9d3795795bd4b319b61df74463f928f
Autor:
Arash Keshavarzi Arshadi, Julia Webb, Milad Salem, Emmanuel Cruz, Stacie Calad-Thomson, Niloofar Ghadirian, Jennifer Collins, Elena Diez-Cecilia, Brendan Kelly, Hani Goodarzi, Jiann Shiun Yuan
Publikováno v:
Frontiers in Artificial Intelligence, Vol 3 (2020)
SARS-COV-2 has roused the scientific community with a call to action to combat the growing pandemic. At the time of this writing, there are as yet no novel antiviral agents or approved vaccines available for deployment as a frontline defense. Underst
Externí odkaz:
https://doaj.org/article/dc313295629747798bfad07f2859c280
Autor:
Arash Keshavarzi Arshadi, Milad Salem, Jennifer Collins, Jiann Shiun Yuan, Debopam Chakrabarti
Publikováno v:
Frontiers in Pharmacology, Vol 10 (2020)
Antimalarial drugs are becoming less effective due to the emergence of drug resistance. Resistance has been reported for all available malaria drugs, including artemisinin, thus creating a perpetual need for alternative drug candidates. The tradition
Externí odkaz:
https://doaj.org/article/178e3e8e90164e7ba5a9322b5c16c916
Autor:
Aminollah Khormali, Jiann-Shiun Yuan
Publikováno v:
IEEE Access, Vol 12, Pp 58114-58127 (2024)
Deepfake detection methods have shown promising results in recognizing forgeries within a given dataset, where training and testing take place on the in-distribution dataset. However, their performance deteriorates significantly when presented with u
Externí odkaz:
https://doaj.org/article/c4497c31ee284f70a112879294805db6
Publikováno v:
Algorithms, Vol 17, Iss 4, p 155 (2024)
The rapid expansion and pervasive reach of the internet in recent years have raised concerns about evolving and adaptable online threats, particularly with the extensive integration of Machine Learning (ML) systems into our daily routines. These syst
Externí odkaz:
https://doaj.org/article/a77769e949d44d979ce522e00a46b2a0
Autor:
Wen Yang, Jiann-Shiun Yuan
Publikováno v:
IEEE Transactions on Device and Materials Reliability. 22:217-222
Autor:
Arash Keshavarzi Arshadi, Milad Salem, Heather Karner, Kristle Garcia, Abolfazl Arab, Jiann Shiun Yuan, Hani Goodarzi
Publikováno v:
bioRxiv
MicroRNAs are recognized as key drivers in many cancers, but targeting them with small molecules remains a challenge. We present RiboStrike, a deep learning framework that identifies small molecules against specific microRNAs. To demonstrate its capa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c9655835cdf32e9c32a82ce1404bf1c5
https://europepmc.org/articles/PMC9882104/
https://europepmc.org/articles/PMC9882104/
Publikováno v:
IEEE Transactions on Device and Materials Reliability. 21:479-485
This paper reports investigation of failure mechanisms of GaN-on-Si power device under electrostatic discharge (ESD) stress using on-wafer transmission-line pulse (TLP) testing. Hot-hole injections under the gate and filament formation in the buffer
Autor:
Jiann-Shiun Yuan, Milad Salem, Amirsaman Mahdavian, Amr A. Oloufa, Alireza Shojaei, Haluk Laman
Publikováno v:
Modelling, Vol 2, Iss 26, Pp 482-513 (2021)
Modelling
Volume 2
Issue 4
Pages 26-513
Modelling
Volume 2
Issue 4
Pages 26-513
Research indicates that the projection of traffic volumes is a valuable tool for traffic management. However, few studies have examined the application of a universal automated framework for car traffic volume prediction. Within this limited literatu