Paper Key : IRJ************213
Author: Vaishnavi N
Date Published: 08 Nov 2024
Abstract
This report explores advanced gene expression analysis using deep learning techniques, focusing on the potential to enhance our understanding of complex biological systems and disease mechanisms. Gene expression analysis traditionally relies on methods like quantitative PCR, microarrays, and RNA sequencing to profile gene activity in various tissues and conditions. However, with the advent of high-dimensional data from next-generation sequencing, traditional computational techniques struggle to handle the vast, intricate datasets effectively. Deep learning, particularly neural network architectures like convolutional neural networks (CNNs) and recurrent neural networks (RNNs), has shown substantial promise in extracting complex patterns from gene expression data, thus enabling more accurate predictions of gene function and behavior. In this study, we applied deep learning models to large gene expression datasets to identify biomarkers and classify gene functions associated with specific biological processes and diseases. Our approach involves preprocessing and normalizing gene expression data, feature selection, and implementing CNN and RNN models to interpret expression patterns. By comparing the performance of different neural network architectures, we aimed to determine the most effective techniques for various gene expression tasks. The findings indicate that deep learning models can significantly improve classification accuracy and identify novel gene interactions, which are challenging to capture with traditional methods. This study contributes to the growing field of computational genomics, showcasing how deep learning can advance gene expression analysis and support personalized medicine efforts by providing insights into the genetic basis of complex diseases.
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