A Neural Network Based Method with Transfer Learning for Genetic Data Analysis

TL;DR

This paper introduces a transfer learning approach combined with expectile neural networks to enhance genetic data analysis, demonstrating improved performance on real datasets by leveraging related task knowledge.

Contribution

It is the first to apply transfer learning to genetic data analysis using expectile neural networks, improving model performance over non-transfer learning methods.

Findings

Transfer learning improves expectile neural network performance on genetic data.

The method outperforms traditional expectile neural networks without transfer learning.

Real data experiments validate the effectiveness of the proposed approach.

Abstract

Transfer learning has emerged as a powerful technique in many application problems, such as computer vision and natural language processing. However, this technique is largely ignored in application to genetic data analysis. In this paper, we combine transfer learning technique with a neural network based method(expectile neural networks). With transfer learning, instead of starting the learning process from scratch, we start from one task that have been learned when solving a different task. We leverage previous learnings and avoid starting from scratch to improve the model performance by passing information gained in different but related task. To demonstrate the performance, we run two real data sets. By using transfer learning algorithm, the performance of expectile neural networks is improved compared to expectile neural network without using transfer learning technique.