Reverse engineering gene regulatory network based on complex-valued ordinary differential equation model

Yang, Bin; Bao, Wenzheng; Zhang, Wei; Wang, Haifeng; Song, Chuandong; Chen, Yuehui; Jiang, Xiuying

doi:10.1186/s12859-021-04367-2

Cited by 9 publications

(4 citation statements)

References 55 publications

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“…interaction networks for genes, transcripts or proteins). With respect to the latter, this may include the alignment of multimodal networks [380][381][382], which can be used for predicting the associations between biological and disease processes [381,382], and the modelling of gene regulator networks using complex-valued ODEs [383], which may be especially well suited to quantum information. Given the breadth of the bioinformatics space, these applications represent a very small subset of the potential emerging application space for quantum algorithm development.…”

Section: Emerging Application Areasmentioning

confidence: 99%

Biology and medicine in the landscape of quantum advantages

Cordier

Sawaya

Guerreschi

et al. 2022

J. R. Soc. Interface.

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Quantum computing holds substantial potential for applications in biology and medicine, spanning from the simulation of biomolecules to machine learning methods for subtyping cancers on the basis of clinical features. This potential is encapsulated by the concept of a quantum advantage, which is contingent on a reduction in the consumption of a computational resource, such as time, space or data. Here, we distill the concept of a quantum advantage into a simple framework to aid researchers in biology and medicine pursuing the development of quantum applications. We then apply this framework to a wide variety of computational problems relevant to these domains in an effort to (i) assess the potential of practical advantages in specific application areas and (ii) identify gaps that may be addressed with novel quantum approaches. In doing so, we provide an extensive survey of the intersection of biology and medicine with the current landscape of quantum algorithms and their potential advantages. While we endeavour to identify specific computational problems that may admit practical advantages throughout this work, the rapid pace of change in the fields of quantum computing, classical algorithms and biological research implies that this intersection will remain highly dynamic for the foreseeable future.

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Section: Emerging Application Areasmentioning

confidence: 99%

Biology and medicine in the landscape of quantum advantages

Cordier

Sawaya

Guerreschi

et al. 2022

J. R. Soc. Interface.

View full text Add to dashboard Cite

show abstract

“…interaction networks for genes, transcripts, or proteins). With respect to the latter, this may include the alignment of multimodal networks [405,406,407], which can be used for predicting the associations between biological and disease processes [406,407], and the modeling of gene regulator networks using complex-valued ODEs [408], which may be especially well suited to quantum information. Given the breadth of the bioinformatics space, these applications represent a very small subset of the potential emerging application space for quantum algorithm development.…”

Section: Bioinformaticsmentioning

confidence: 99%

Biology and medicine in the landscape of quantum advantages

Cordier¹,

Sawaya²,

Guerreschi³

et al. 2021

Preprint

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Quantum computing holds significant potential for applications in biology and medicine, spanning from the simulation of biomolecules to machine learning approaches for subtyping cancers on the basis of clinical features. This potential is encapsulated by the concept of a quantum advantage, which is typically contingent on a reduction in the consumption of a computational resource, such as time, space, or data. Here, we distill the concept of a quantum advantage into a simple framework that we hope will aid researchers in biology and medicine pursuing the development of quantum applications. We then apply this framework to a wide variety of computational problems relevant to these domains in an effort to i) assess the potential of quantum advantages in specific application areas and ii) identify gaps that may be addressed with novel quantum approaches. Bearing in mind the rapid pace of change in the fields of quantum computing and classical algorithms, we aim to provide an extensive survey of applications in biology and medicine that may lead to practical quantum advantages.

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“…An example can be found in [ 14 ], where a metaheuristic was used to find the parameters of an S-system model describing the dynamics of gene expression. Another example can be found in [ 15 ], where a complex-valued ordinary differential equation model was created using genetic programming. In addition, it is possible to directly predict the interaction between genes using a gene expression dataset.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach to Simulate Gene Expression and Infer Gene Regulatory Networks

Zito

Cutello

Pavone

2023

Entropy

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The ability to simulate gene expression and infer gene regulatory networks has vast potential applications in various fields, including medicine, agriculture, and environmental science. In recent years, machine learning approaches to simulate gene expression and infer gene regulatory networks have gained significant attention as a promising area of research. By simulating gene expression, we can gain insights into the complex mechanisms that control gene expression and how they are affected by various environmental factors. This knowledge can be used to develop new treatments for genetic diseases, improve crop yields, and better understand the evolution of species. In this article, we address this issue by focusing on a novel method capable of simulating the gene expression regulation of a group of genes and their mutual interactions. Our framework enables us to simulate the regulation of gene expression in response to alterations or perturbations that can affect the expression of a gene. We use both artificial and real benchmarks to empirically evaluate the effectiveness of our methodology. Furthermore, we compare our method with existing ones to understand its advantages and disadvantages. We also present future ideas for improvement to enhance the effectiveness of our method. Overall, our approach has the potential to greatly improve the field of gene expression simulation and gene regulatory network inference, possibly leading to significant advancements in genetics.

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Reverse engineering gene regulatory network based on complex-valued ordinary differential equation model

Cited by 9 publications

References 55 publications

Biology and medicine in the landscape of quantum advantages

Biology and medicine in the landscape of quantum advantages

Biology and medicine in the landscape of quantum advantages

A Machine Learning Approach to Simulate Gene Expression and Infer Gene Regulatory Networks

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