Machine Learning Techniques for Genomic Data Analysis and Computer Vision Integration

EasyChair Preprint 12449

Abstract

In recent years, the integration of machine learning techniques has significantly advanced both genomic data analysis and computer vision. This paper proposes a novel approach that harnesses the synergy between these two domains to enhance our understanding of complex biological processes. Genomic data analysis plays a pivotal role in deciphering the genetic basis of diseases, while computer vision techniques excel in extracting meaningful patterns from visual data. By combining these methodologies, we aim to provide a comprehensive framework for analyzing genomic data in a visual context. Our proposed framework begins with preprocessing genomic data to extract relevant features, such as gene expression levels or genetic variations. Subsequently, we employ computer vision algorithms to transform these features into visual representations, leveraging techniques such as dimensionality reduction and image generation. Through the application of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), we extract hierarchical features and capture temporal dependencies within the genomic data. Furthermore, we explore transfer learning approaches to adapt pre-trained models from computer vision tasks to genomic data analysis, thereby enhancing the efficiency and generalization of our framework. Additionally, we investigate the integration of attention mechanisms to prioritize salient genomic regions for further analysis, facilitating the identification of key genetic markers associated with specific phenotypes.

Keyphrases: Genomic Data Analysis, computer vision, machine learning

@booklet{EasyChair:12449,
  author    = {Jane Elsa and Thomson Eric},
  title     = {Machine Learning Techniques for Genomic Data Analysis and Computer Vision Integration},
  howpublished = {EasyChair Preprint 12449},
  year      = {EasyChair, 2024}}