SimNetX: tinkering with patient similarity networks to understand biomedical data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00098892%3A_____%2F25%3A10159531" target="_blank" >RIV/00098892:_____/25:10159531 - isvavai.cz</a>

Výsledek na webu

<a href="https://appliednetsci.springeropen.com/articles/10.1007/s41109-025-00743-6" target="_blank" >https://appliednetsci.springeropen.com/articles/10.1007/s41109-025-00743-6</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s41109-025-00743-6" target="_blank" >10.1007/s41109-025-00743-6</a>

Jazyk výsledku

angličtina

Název v původním jazyce

SimNetX: tinkering with patient similarity networks to understand biomedical data

Popis výsledku v původním jazyce

Analyzing complex biomedical data often requires statistical and machine learning expertise, creating barriers for clinicians, laboratory scientists, and other non-technical users. Patient similarity networks (PSNs) offer an intuitive way to explore patient relationships and patterns, making data interpretation more accessible. However, constructing and analyzing PSNs typically involves multiple software tools or programming skills, limiting their usability for those without technical expertise. In this article, we introduce an approach that enables non-technical users to analyze biomedical data through PSNs without requiring programming knowledge. By integrating key functionalities-such as transforming vector-based data into networks, interactively exploring patient relationships, and applying statistical insights-this approach bridges the gap between complex data analysis and domain experts. To facilitate this, we provide a tool designed to implement these methods in an intuitive, interactive environment. We demonstrate its practical application using two well-known datasets as well as two real-world biomedical datasets, showing how non-experts can generate hypotheses and extract meaningful insights through visual exploration and built-in simple statistical analysis.

Název v anglickém jazyce

SimNetX: tinkering with patient similarity networks to understand biomedical data

Popis výsledku anglicky

Analyzing complex biomedical data often requires statistical and machine learning expertise, creating barriers for clinicians, laboratory scientists, and other non-technical users. Patient similarity networks (PSNs) offer an intuitive way to explore patient relationships and patterns, making data interpretation more accessible. However, constructing and analyzing PSNs typically involves multiple software tools or programming skills, limiting their usability for those without technical expertise. In this article, we introduce an approach that enables non-technical users to analyze biomedical data through PSNs without requiring programming knowledge. By integrating key functionalities-such as transforming vector-based data into networks, interactively exploring patient relationships, and applying statistical insights-this approach bridges the gap between complex data analysis and domain experts. To facilitate this, we provide a tool designed to implement these methods in an intuitive, interactive environment. We demonstrate its practical application using two well-known datasets as well as two real-world biomedical datasets, showing how non-experts can generate hypotheses and extract meaningful insights through visual exploration and built-in simple statistical analysis.

Druh

J_imp - Článek v periodiku v databázi Web of Science

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 periodika

Applied Network Science

ISSN

—

e-ISSN

2364-8228

Svazek periodika

10

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

27

Strana od-do

54

Kód UT WoS článku

001605258000001

EID výsledku v databázi Scopus

2-s2.0-105020590957