Zobrazeno 1 - 10
of 227
pro vyhledávání: '"Koen Bertels"'
Publikováno v:
BMC Bioinformatics, Vol 21, Iss S13, Pp 1-17 (2020)
Abstract Background In Overlap-Layout-Consensus (OLC) based de novo assembly, all reads must be compared with every other read to find overlaps. This makes the process rather slow and limits the practicality of using de novo assembly methods at a lar
Externí odkaz:
https://doaj.org/article/3a6acb84b405492291b56dd2b3868836
Autor:
Koen Bertels, A. Sarkar, T. Hubregtsen, M. Serrao, A.A. Mouedenne, A. Yadav, A. Krol, I. Ashraf, C. Garcia Almudever
Publikováno v:
IEEE Transactions on Quantum Engineering, Vol 1, Pp 1-17 (2020)
This article presents the definition and implementation of a quantum computer architecture to enable creating a new computational device—a quantum computer as an accelerator. A key question addressed is what such a quantum computer is and how it re
Externí odkaz:
https://doaj.org/article/39a240d57bb24143bffc0d6a91174928
Publikováno v:
BMC Bioinformatics, Vol 20, Iss 1, Pp 1-4 (2019)
Following publication of the original article [1], the author requested changes to the figures 4, 7, 8, 9, 12 and 14 to align these with the text. The corrected figures are supplied below.
Externí odkaz:
https://doaj.org/article/5ea0ce5b29b640ce961f098b2e2d0aed
Publikováno v:
BMC Bioinformatics, Vol 20, Iss 1, Pp 1-20 (2019)
Abstract Background Due the computational complexity of sequence alignment algorithms, various accelerated solutions have been proposed to speedup this analysis. NVBIO is the only available GPU library that accelerates sequence alignment of high-thro
Externí odkaz:
https://doaj.org/article/099aa14c6d8640cb95482a2a5e010bce
Publikováno v:
BMC Genomics, Vol 20, Iss S2, Pp 103-116 (2019)
Abstract Background Pairwise sequence alignment is widely used in many biological tools and applications. Existing GPU accelerated implementations mainly focus on calculating optimal alignment score and omit identifying the optimal alignment itself.
Externí odkaz:
https://doaj.org/article/f81c2b5f49c740c5b14c2e943fda801e
Publikováno v:
PLoS ONE, Vol 16, Iss 4, p e0249850 (2021)
In this article, we present QuASeR, a reference-free DNA sequence reconstruction implementation via de novo assembly on both gate-based and quantum annealing platforms. This is the first time this important application in bioinformatics is modeled us
Externí odkaz:
https://doaj.org/article/7ae53ee3fdd74e28b83892537766064c
Publikováno v:
Applied Sciences, Vol 12, Iss 2, p 759 (2022)
Unitary decomposition is a widely used method to map quantum algorithms to an arbitrary set of quantum gates. Efficient implementation of this decomposition allows for the translation of bigger unitary gates into elementary quantum operations, which
Externí odkaz:
https://doaj.org/article/942def9f1f254346b17a8208440a4a4d
Publikováno v:
Applied Sciences, Vol 11, Iss 6, p 2696 (2021)
Inferring algorithmic structure in data is essential for discovering causal generative models. In this research, we present a quantum computing framework using the circuit model, for estimating algorithmic information metrics. The canonical computati
Externí odkaz:
https://doaj.org/article/e68834b5ef8648b988338e3c918b85d0
Publikováno v:
Evolutionary Bioinformatics, Vol 14 (2018)
GATK HaplotypeCaller (HC) is a popular variant caller, which is widely used to identify variants in complex genomes. However, due to its high variants detection accuracy, it suffers from long execution time. In GATK HC, the pair-HMMs forward algorith
Externí odkaz:
https://doaj.org/article/73ffa8cdb29d4e009295751d1e8f37e4
Autor:
Christophe Vuillot, Lingling Lao, Ben Criger, Carmen García Almudéver, Koen Bertels, Barbara M Terhal
Publikováno v:
New Journal of Physics, Vol 21, Iss 3, p 033028 (2019)
The large-scale execution of quantum algorithms requires basic quantum operations to be implemented fault-tolerantly. The most popular technique for accomplishing this, using the devices that can be realized in the near term, uses stabilizer codes wh
Externí odkaz:
https://doaj.org/article/237f9f48e7f443bebbfec3d9b77d6349