A Stable, Unified Density Controlled Memetic Algorithm for Gene Regulatory Network Reconstruction Based on Sparse Fuzzy Cognitive Maps

Wang, Yilan; Liu, Jing

doi:10.1007/s11063-019-10056-2

Neural Process Lett

2019

DOI: 10.1007/s11063-019-10056-2

|View full text |Cite

A Stable, Unified Density Controlled Memetic Algorithm for Gene Regulatory Network Reconstruction Based on Sparse Fuzzy Cognitive Maps

Yilan Wang

Jing Liu

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2022

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

DESIGN AND DEVELOPMENT OF ADVANCED SIMILARITY MEASURE FOR RECONSTRUCTING GRN USING mRNA EXPRESSION PROFILES

Bhyratae

Mangla²

2022

Biomed. Eng. Appl. Basis Commun.

View full text Add to dashboard Cite

Gene Regulatory Networks (GRNs) reconstruction aims to infer relationships of potential regulation among the genes. With the rapid growth of the biotechnology, such as Ribonucleic acid (RNA)-sequencing and gene chip microarray, the generated high-throughput data provide gene–gene interaction relationships with more opportunities based on gene expression data. Several approaches are introduced to reconstruct the GRNs, but low accuracy is a major drawback. Hence, this paper introduces the hybrid distance measure and the Pearson’s correlation coefficient for reconstructing GRN. The hybrid distance, such as Tversky index, Tanimoto similarity, and Minkowski distance, is employed to connect the edges. The asymmetric partial correlation network is introduced for determining two influence functions for every pair, and edge direction is determined among them. However, the direction of edges is unknown usually and seems difficult to be identified based on gene expression data. Thus, it extends the data processing inequality applying in the directed network for removing the transitive interactions. The influence value of every node is calculated for identifying the significant regulator. The performance of the proposed Hybrid Distance_Entropy based GRN Reconstruction method is analyzed in terms of correlation, reconstruction error, precision, and recall, which provides superior results with values 0.9450, 0.00052, 0.9095, and 0.8913 based on dataset-1.

show abstract

DESIGN AND DEVELOPMENT OF ADVANCED SIMILARITY MEASURE FOR RECONSTRUCTING GRN USING mRNA EXPRESSION PROFILES

Bhyratae

Mangla²

2022

Biomed. Eng. Appl. Basis Commun.

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

A Stable, Unified Density Controlled Memetic Algorithm for Gene Regulatory Network Reconstruction Based on Sparse Fuzzy Cognitive Maps

Cited by 1 publication

References 52 publications

DESIGN AND DEVELOPMENT OF ADVANCED SIMILARITY MEASURE FOR RECONSTRUCTING GRN USING mRNA EXPRESSION PROFILES

DESIGN AND DEVELOPMENT OF ADVANCED SIMILARITY MEASURE FOR RECONSTRUCTING GRN USING mRNA EXPRESSION PROFILES

Contact Info

Product

Resources

About