SimNetX: Interactive Support for Biomedical Data Analysis Using Patient Similarity Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00098892%3A_____%2F25%3A10159371" target="_blank" >RIV/00098892:_____/25:10159371 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27240/25:10260260

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-031-82439-5_1" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-82439-5_1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-82439-5_1" target="_blank" >10.1007/978-3-031-82439-5_1</a>

Jazyk výsledku

angličtina

Název v původním jazyce

SimNetX: Interactive Support for Biomedical Data Analysis Using Patient Similarity Networks

Popis výsledku v původním jazyce

Patient similarity networks (PSNs) are a widely used tool in biomedical data analysis. The most powerful feature of PSNs (and all networks in general) is the ability to visualize relatively complex data in a way that people can understand without expertise in statistics and machine learning. This aspect of data analysis is particularly important in biomedical data analysis, where data analysts, clinicians, and people from laboratories work in collaborative teams. However, working with PSNs requires steps that are usually solved in different systems or using programming libraries, e.g., in R or Python. This paper presents a tool whose design results from several years of experience with PSNs in biomedical data analysis. The tool focuses on the essential tasks of transforming vector data into PSNs and exploring them in detail, as well as on a high level of interactivity, e.g. allowing the formulation of preliminary hypotheses. We demonstrate the practical use of the tool on a well-known biological dataset.

Název v anglickém jazyce

SimNetX: Interactive Support for Biomedical Data Analysis Using Patient Similarity Networks

Popis výsledku anglicky

Patient similarity networks (PSNs) are a widely used tool in biomedical data analysis. The most powerful feature of PSNs (and all networks in general) is the ability to visualize relatively complex data in a way that people can understand without expertise in statistics and machine learning. This aspect of data analysis is particularly important in biomedical data analysis, where data analysts, clinicians, and people from laboratories work in collaborative teams. However, working with PSNs requires steps that are usually solved in different systems or using programming libraries, e.g., in R or Python. This paper presents a tool whose design results from several years of experience with PSNs in biomedical data analysis. The tool focuses on the essential tasks of transforming vector data into PSNs and exploring them in detail, as well as on a high level of interactivity, e.g. allowing the formulation of preliminary hypotheses. We demonstrate the practical use of the tool on a well-known biological dataset.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Complex Networks &amp; Their Applications XIII

ISBN

978-3-031-82438-8

ISSN

1860-949X

e-ISSN

1860-9503

Počet stran výsledku

12

Strana od-do

3-14

Název nakladatele

Springer Cham

Místo vydání

Cham

Místo konání akce

Istanbul

Datum konání akce

10. 12. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001489045000001