A hybrid inductive model for gene expression data processing using spectral clustering

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13440%2F24%3A43898913" target="_blank" >RIV/44555601:13440/24:43898913 - isvavai.cz</a>

Result on the web

<a href="https://ceur-ws.org/Vol-3892/paper2.pdf" target="_blank" >https://ceur-ws.org/Vol-3892/paper2.pdf</a>

DOI - Digital Object Identifier

—

Result language

angličtina

Original language name

A hybrid inductive model for gene expression data processing using spectral clustering

Original language description

One of the key directions in modern bioinformatics is the development of systems for diagnosing various diseases usinggene expression data. Clustering gene expression profiles is a critical step in disease diagnosis systems. In this study, wepropose a hybrid inductive model for clustering gene expression profiles using the spectral clustering algorithm. Theimplementation of this model aims to reduce reproducibility errors by serializing the data processing flow and optimizingclustering based on both internal and external quality criteria. The model is presented as a block diagram, and its practicalimplementation has demonstrated the high effectiveness of the proposed approach. The model&apos;s performance wasevaluated using a convolutional neural network. The experimental dataset consisted of gene expression values assigned tothe identified clusters. The simulation results indicate that the highest classification accuracy was achieved with a three-cluster structure, which corresponded to the highest balance between internal and external clustering quality criteria. Thesefindings create opportunities for enhancing existing gene expression clustering models through more precise tuning ofclustering algorithm hyperparameters, guided by the principles of inductive methods for analyzing complex systems

Czech name

—

Czech description

—

Type

W - Workshop organization

CEP classification

—

OECD FORD branch

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

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

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

Event location

Birmingham

Event country

GB - UNITED KINGDOM

Event starting date

—

Event ending date

—

Total number of attendees

85

Foreign attendee count

64

Type of event by attendee nationality

WRD - Celosvětová akce