Revolutionizing Drug Discovery: Integrating Spatial Transcriptomics with Advanced Computer Vision Techniques
Keywords: Spatial transcriptomics, computer vision, deep learning, U-Net, biomarker discovery, drug discovery, multi-task learning
TL;DR: We integrate spatial transcriptomics with advanced computer vision techniques to revolutionize drug discovery through accurate biomarker identification and noise-resilient analysis.
Abstract: Spatial transcriptomics has emerged as a transformative technology for mapping gene expression within tissue contexts, offering unprecedented insights into disease mechanisms. However, extracting actionable insights from these high-dimensional datasets remains challenging due to their complexity and noise. In this paper, we propose a novel framework that integrates spatial transcriptomics with advanced computer vision techniques to identify therapeutic targets in drug discovery. Our approach leverages deep learning-based segmentation and graph neural networks (GNNs) to capture spatial relationships and enhance interpretability. Experiments on benchmark datasets demonstrate significant improvements in identifying disease-specific biomarkers compared to traditional methods. This work underscores the potential of computer vision to revolutionize drug discovery by enabling faster and more accurate target identification.
Submission Type: Original Work
Submission Number: 4
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