High-performance cartesian genetic programming on GPU for the inference of gene regulatory networks using scRNA-seq time-series data-Reference-Cited by-同舟云学术

High-performance cartesian genetic programming on GPU for the inference of gene regulatory networks using scRNA-seq time-series data

Published:2022-07-09 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Genetic and Evolutionary Computation Conference Companion
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Author:

Prachedes Luciana Nascimento Santana¹,Silva José Eduardo Henriques da¹,Bernardino Heder Soares¹,de Oliveira Itamar Leite¹

Affiliation:

1. Federal University of Juiz de Fora, Juiz de Fora, MG, Brazil

Funder

UFJF

Capes

FAPEMIG

CNPq

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3520304.3534032

Reference37 articles.

1. Tutorial: guidelines for the computational analysis of single-cell RNA sequencing data;Andrews Tallulah S;Nature Protocols,2020

2. Accelerated parallel genetic programming tree evaluation with OpenCL

3. Computational inference of gene regulatory networks: Approaches, limitations and opportunities

4. The nature of systems biology

5. Lian En Chai , Swee Kuan Loh , Swee Thing Low, Mohd Saberi Mohamad, Safaai Deris, and Zalmiyah Zakaria. 2014 . A review on the computational approaches for gene regulatory network construction. Computers in biology and medicine 48 (2014), 55--65. Lian En Chai, Swee Kuan Loh, Swee Thing Low, Mohd Saberi Mohamad, Safaai Deris, and Zalmiyah Zakaria. 2014. A review on the computational approaches for gene regulatory network construction. Computers in biology and medicine 48 (2014), 55--65.

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