Deep Learning for Polycystic Kidney Disease: Utilizing Neural Networks for Accurate and Early Detection through Gene Expression Analysis

TL;DR

This paper presents a deep learning approach using neural networks to analyze gene expression data for early and accurate detection of Polycystic Kidney Disease, potentially improving patient outcomes.

Contribution

It introduces a neural network model specifically designed for PKD detection through gene expression analysis, highlighting its robustness and accuracy.

Findings

Achieved high prediction accuracy for PKD detection

Identified key gene processes and functions affected by PKD

Demonstrated robustness of the neural network model

Abstract

With Polycystic Kidney Disease (PKD) potentially leading to fatal complications in patients due to the formation of cysts in kidneys, early detection of PKD is crucial for effective management of the condition. However, the various patient-specific factors that play a role in the diagnosis make it an intricate puzzle for clinicians to solve, leading to possible kidney failure. Therefore, in this study we aim to utilize a deep learning-based approach for early disease detection through gene expression analysis. The devised neural network is able to achieve accurate and robust prediction results for possible PKD in kidneys, thereby improving patient outcomes. Furthermore, by conducting a gene ontology analysis, we were able to predict the top gene processes and functions that PKD may affect.