Zobrazeno 1 - 10
of 62
pro vyhledávání: '"Canh Hao Nguyen"'
Autor:
Duc Quang Le, Tien Anh Nguyen, Son Hoang Nguyen, Tam Thi Nguyen, Canh Hao Nguyen, Huong Thanh Phung, Tho Huu Ho, Nam S. Vo, Trang Nguyen, Hoang Anh Nguyen, Minh Duc Cao
Publikováno v:
Genome Biology, Vol 25, Iss 1, Pp 1-16 (2024)
Abstract Pangenome inference is an indispensable step in bacterial genomics, yet its scalability poses a challenge due to the rapid growth of genomic collections. This paper presents PanTA, a software package designed for constructing pangenomes of l
Externí odkaz:
https://doaj.org/article/e0828885ef8d4eeea225cd9489228609
Autor:
Duc Quang Le, Tam Thi Nguyen, Canh Hao Nguyen, Tho Huu Ho, Nam S. Vo, Trang Nguyen, Hoang Anh Nguyen, Le Sy Vinh, Thanh Hai Dang, Minh Duc Cao, Son Hoang Nguyen
Publikováno v:
BMC Genomics, Vol 25, Iss 1, Pp 1-8 (2024)
Abstract Whole genome analysis for microbial genomics is critical to studying and monitoring antimicrobial resistance strains. The exponential growth of microbial sequencing data necessitates a fast and scalable computational pipeline to generate the
Externí odkaz:
https://doaj.org/article/c55dde5b0939434e803c2aea563400fa
Autor:
Duc Quang Le, Son Hoang Nguyen, Tam Thi Nguyen, Canh Hao Nguyen, Tho Huu Ho, Nam S. Vo, Trang Nguyen, Hoang Anh Nguyen, Minh Duc Cao
Publikováno v:
BMC Bioinformatics, Vol 25, Iss 1, Pp 1-10 (2024)
Abstract We have developed AMRViz, a toolkit for analyzing, visualizing, and managing bacterial genomics samples. The toolkit is bundled with the current best practice analysis pipeline allowing researchers to perform comprehensive analysis of a coll
Externí odkaz:
https://doaj.org/article/d9ee2209a9ce47a9a5fd324ef3c66e7d
Autor:
Van Hoan Do, Van Sang Nguyen, Son Hoang Nguyen, Duc Quang Le, Tam Thi Nguyen, Canh Hao Nguyen, Tho Huu Ho, Nam S. Vo, Trang Nguyen, Hoang Anh Nguyen, Minh Duc Cao
Publikováno v:
iScience, Vol 27, Iss 9, Pp 110623- (2024)
Summary: Machine learning has the potential to be a powerful tool in the fight against antimicrobial resistance (AMR), a critical global health issue. Machine learning can identify resistance mechanisms from DNA sequence data without prior knowledge.
Externí odkaz:
https://doaj.org/article/ee0b497cebc544b495aed8a4c7ff436c
Autor:
Hiroto Kaneko, Romain Blanc-Mathieu, Hisashi Endo, Samuel Chaffron, Tom O. Delmont, Morgan Gaia, Nicolas Henry, Rodrigo Hernández-Velázquez, Canh Hao Nguyen, Hiroshi Mamitsuka, Patrick Forterre, Olivier Jaillon, Colomban de Vargas, Matthew B. Sullivan, Curtis A. Suttle, Lionel Guidi, Hiroyuki Ogata
Publikováno v:
iScience, Vol 24, Iss 1, Pp 102002- (2021)
Summary: The biological carbon pump, in which carbon fixed by photosynthesis is exported to the deep ocean through sinking, is a major process in Earth's carbon cycle. The proportion of primary production that is exported is termed the carbon export
Externí odkaz:
https://doaj.org/article/f4f8af6043f1453594aba77b8c433305
Publikováno v:
Austrian Journal of Statistics, Vol 48, Iss 5 (2019)
A graph random walk is presented. It is derived from the p-Laplacian similarly to the derivation of the canonical random walk from the Laplacian. This variant enables quicker exploration while still sticking to the connection constraints given by the
Externí odkaz:
https://doaj.org/article/8c1fe0e8d9c746b99728a03f092654ce
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. :1-6
Publikováno v:
Bioinformatics (Oxford, England). 38(Suppl 1)
Motivation Predicting side effects of drug–drug interactions (DDIs) is an important task in pharmacology. The state-of-the-art methods for DDI prediction use hypergraph neural networks to learn latent representations of drugs and side effects to ex
Publikováno v:
Neurocomputing. 424:143-159
Exploiting prior/human knowledge is an effective way to enhance Bayesian models, especially in cases of sparse or noisy data, for which building an entirely new model is not always possible. There is a lack of studies on the effect of external prior
Autor:
Canh Hao Nguyen
Publikováno v:
Journal of Systems Science and Systems Engineering. 29:440-453
Biological domain has been blessed with more and more data from biotechnologies as well as data integration tools. In the renaissance of machine learning and artificial intelligence, there is so much promise of data-driven biological knowledge discov