<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Sequencing technology continues to evolve, and pharmacogenomics is increasingly pivotal in the pursuit of personalized medicine. This study delineates the personalized genomics model into two core modules for framework construction: data preprocessing and prediction. Within the data preprocessing module, a denoising submodule and a genomic feature distribution alignment module are dedicated to processing the genomic features associated with diseases and assimilating them into the spatial feature distribution of the model. The prediction module employs a fully connected neural network alongside a graph convolutional neural network to forecast drug dosages based on the disease's genomic features. We propose clinical applications of personalized genomics models across three distinct pathways. To assess the practical impact of these models, experiments were conducted focusing on their clinical application. After 14 days of medication administration, the International Normalized Ratio (INR) value for the drug model group reached 2.67, surpassing that of the conventional treatment group, with nearly 60% of participants achieving the target range. Furthermore, in evaluating the safety and efficacy of the clinical application of the genomics model, the incidence of serious bleeding events in the drug model group was recorded at 16.67%, which is below the standard allowable probability of 23.81%. Therefore, the personalized genomics model introduced in this study meets the established safety and efficacy standards.</p>
</abstract>

Sequencing technology continues to evolve, and pharmacogenomics is increasingly pivotal in the pursuit of personalized medicine. This study delineates the personalized genomics model into two core modules for framework construction: data preprocessing and prediction. Within the data preprocessing module, a denoising submodule and a genomic feature distribution alignment module are dedicated to processing the genomic features associated with diseases and assimilating them into the spatial feature distribution of the model. The prediction module employs a fully connected neural network alongside a graph convolutional neural network to forecast drug dosages based on the disease's genomic features. We propose clinical applications of personalized genomics models across three distinct pathways. To assess the practical impact of these models, experiments were conducted focusing on their clinical application. After 14 days of medication administration, the International Normalized Ratio (INR) value for the drug model group reached 2.67, surpassing that of the conventional treatment group, with nearly 60% of participants achieving the target range. Furthermore, in evaluating the safety and efficacy of the clinical application of the genomics model, the incidence of serious bleeding events in the drug model group was recorded at 16.67%, which is below the standard allowable probability of 23.81%. Therefore, the personalized genomics model introduced in this study meets the established safety and efficacy standards.

Construction of personalized genomics model and clinical application in precision medicine

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Sequencing technology continues to evolve, and pharmacogenomics is increasingly pivotal in the pursuit of personalized medicine. This study delineates the...

Construction of personalized genomics model and clinical application in precision medicine

Publicado en línea: 05 ago 2024

Páginas: -

Recibido: 05 abr 2024

Aceptado: 30 jun 2024

DOI: https://doi.org/10.2478/amns-2024-1891

Palabras clave
Genomics model, Spatial feature distribution, Fully connected neural network, Graph convolutional neural network, Clinical application.

© 2024 Haoyu Li., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Construction of personalized genomics model and clinical application in precision medicine

Publicado en línea: 05 ago 2024

Páginas: -

Recibido: 05 abr 2024

Aceptado: 30 jun 2024

DOI: https://doi.org/10.2478/amns-2024-1891

Palabras claveGenomics model, Spatial feature distribution, Fully connected neural network, Graph convolutional neural network, Clinical application.

© 2024 Haoyu Li., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Palabras clave
Genomics model, Spatial feature distribution, Fully connected neural network, Graph convolutional neural network, Clinical application.