Streamflow data analysis using persistent homology

Musa, Syed Mohamad Sadiq Syed; Noorani, Mohd Salmi Md; Razak, Fatimah Abdul; Ismail, Munira; Alias, Mohd Almie

doi:10.1063/1.5111228

The 2018 UKM FST POSTGRADUATE COLLOQUIUM: Proceedings of the Universiti Kebangsaan Malaysia, Faculty of Science and Technology 2019

DOI: 10.1063/1.5111228

|View full text |Cite

Streamflow data analysis using persistent homology

Syed Mohamad Sadiq Syed Musa¹,

Mohd Salmi Md Noorani²,

Fatimah Abdul Razak³

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Enhancing the Vietoris–Rips simplicial complex for topological data analysis: applications in cancer gene expression datasets

Mashatola,

Kader,

Abdulla

et al. 2024

Int J Data Sci Anal

View full text Add to dashboard Cite

The aim of this study is to enhance the extraction of informative features from complex data through the application of topological data analysis (TDA) using novel topological overlapping measures. Topological data analysis has emerged as a promising methodology for extracting meaningful insights from complex datasets. Existing approaches in TDA often involve extrapolating data points using distance correlation measures, which subsequently constrain downstream predictive tasks. Our objective is to improve the construction of the Vietoris–Rips simplicial complex by introducing topological overlapping measures. These measures take into account the interplay of direct connection strengths and shared neighbours, leading to the identification of persistent topological features. We propose the utilisation of topological overlapping measures to optimise the construction of the Vietoris–Rips simplicial complex, offering a more refined representation of complex data structures. The application of topological overlapping measures results in the identification of plentiful persistent topological features. This enhancement contributes to an improvement of up to 20% in cancer phenotype prediction across different cancer types. Our study demonstrates the effectiveness of utilising topological overlapping measures in optimising the construction of the Vietoris–Rips simplicial complex. The identified persistent topological features significantly enhance the predictive accuracy of cancer phenotypes. This novel approach has the potential to advance the field of topological data analysis and improve our understanding of complex data structures, particularly in the context of cancer research and predictive modelling. Further exploration and application of these measures may yield valuable insights in various domains dealing with intricate datasets.

show abstract

Enhancing the Vietoris–Rips simplicial complex for topological data analysis: applications in cancer gene expression datasets

Mashatola,

Kader,

Abdulla

et al. 2024

Int J Data Sci Anal

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.

Streamflow data analysis using persistent homology

Cited by 1 publication

References 16 publications

Enhancing the Vietoris–Rips simplicial complex for topological data analysis: applications in cancer gene expression datasets

Enhancing the Vietoris–Rips simplicial complex for topological data analysis: applications in cancer gene expression datasets

Contact Info

Product

Resources

About