Phylogenetic tree reconstruction via graph cut presented using a quantum-inspired computer

Onodera, Wataru; Hara, Nobuyuki; Aoki, Shiho; Asahi, Toru; Sawamura, Naoya

doi:10.1016/j.ympev.2022.107636

Cited by 6 publications

(9 citation statements)

References 35 publications

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“…We have shown that

consistently cuts only a single node, despite generating phylogenetic trees under various conditions (shown in Fig 2 A, B of Onodera et al. [1] ). Therefore, a constraint term was added to the second term of

to control the number of nodes to be partitioned.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 78%

“…For further analysis of the accuracy of the reconstructed phylogenetic tree, see Onodera et al. [1] . First, the rtree function under the default parameters of the ape package v5.6-1 or the sim.bd.taxa function (lambda = 1, mu =1, frac=1, complete=FALSE, stochsampling=FALSE) of the TreeSim package v2.4 was used to generate a phylogenetic tree [3 , 11] .…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

“…The normalized minimum cut using the Digital Annealer (NMcutDA) method recursively cuts a group of sequences into two and constructs a phylogenetic tree from the subclusters [1] . The normalized cut value (Ncut energy; defined in Shi and Malik 2000 [1] and explained in Eq. 4 ), a criterion for graph cut, was used for clustering criterion.…”

Section: Data Descriptionmentioning

confidence: 99%

“…Here, increasing the number of taxa increases the complexity of the minimum-cut problem and generally makes finding the minimum cut more difficult. Additionally, increasing the average branch length of the phylogenetic tree is equivalent to decreasing the similarity between sequences, thereby making construction of the phylogenetic tree more difficult [1] .

Fig.…”

Section: Data Descriptionmentioning

confidence: 99%

“…A comparison of the Ncut energy between the methods is shown in Figure 3 A by Onodera et al. [1] . Next, the sum of the squared pairwise distances of the sequences within the same cluster was calculated for the similarity matrix, which is a criterion for k-means clustering ( Figure 2 ).…”

Section: Data Descriptionmentioning

confidence: 99%

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Data on the solution and processing time reached when constructing a phylogenetic tree using a quantum-inspired computer

et al. 2023

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“…We have shown that

to control the number of nodes to be partitioned.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 78%

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Section: Data Descriptionmentioning

confidence: 99%

Fig.…”

Section: Data Descriptionmentioning

confidence: 99%

Section: Data Descriptionmentioning

confidence: 99%

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Data on the solution and processing time reached when constructing a phylogenetic tree using a quantum-inspired computer

et al. 2023

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Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics

et al. 2023

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Modern biological science is trying to solve the fundamental complex problems of molecular biology, which include protein folding, drug discovery, simulation of macromolecular structure, genome assembly, and many more. Currently, quantum computing (QC), a rapidly emerging technology exploiting quantum mechanical phenomena, has developed to address current significant physical, chemical, biological issues, and complex questions. The present review discusses quantum computing technology and its status in solving molecular biology problems, especially in the next-generation computational biology scenario. First, the article explained the basic concept of quantum computing, the functioning of quantum systems where information is stored as qubits, and data storage capacity using quantum gates. Second, the review discussed quantum computing components, such as quantum hardware, quantum processors, and quantum annealing. At the same time, article also discussed quantum algorithms, such as the grover search algorithm and discrete and factorization algorithms. Furthermore, the article discussed the different applications of quantum computing to understand the next-generation biological problems, such as simulation and modeling of biological macromolecules, computational biology problems, data analysis in bioinformatics, protein folding, molecular biology problems, modeling of gene regulatory networks, drug discovery and development, mechano-biology, and RNA folding. Finally, the article represented different probable prospects of quantum computing in molecular biology. Supplementary Information The online version contains supplementary material available at 10.1007/s12033-023-00765-4.

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A primer for quantum computing and its applications to healthcare and biomedical research

Durant,

Knight,

Nelson

et al. 2024

Journal of the American Medical Informatics Association

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Objectives To introduce quantum computing technologies as a tool for biomedical research and highlight future applications within healthcare, focusing on its capabilities, benefits, and limitations. Target Audience Investigators seeking to explore quantum computing and create quantum-based applications for healthcare and biomedical research. Scope Quantum computing requires specialized hardware, known as quantum processing units, that use quantum bits (qubits) instead of classical bits to perform computations. This article will cover (1) proposed applications where quantum computing offers advantages to classical computing in biomedicine; (2) an introduction to how quantum computers operate, tailored for biomedical researchers; (3) recent progress that has expanded access to quantum computing; and (4) challenges, opportunities, and proposed solutions to integrate quantum computing in biomedical applications.

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Phylogenetic tree reconstruction via graph cut presented using a quantum-inspired computer

Cited by 6 publications

References 35 publications

Data on the solution and processing time reached when constructing a phylogenetic tree using a quantum-inspired computer

Data on the solution and processing time reached when constructing a phylogenetic tree using a quantum-inspired computer

Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics

A primer for quantum computing and its applications to healthcare and biomedical research

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