Deep Learning Approaches for Blood Disease Diagnosis Across Hematopoietic Lineages

• URL: http://arxiv.org/abs/2503.20049v1
• Date: Tue, 25 Mar 2025 20:11:10 GMT
• Title: Deep Learning Approaches for Blood Disease Diagnosis Across Hematopoietic Lineages
• Authors: Gabriel Bo, Justin Gu, Christopher Sun,
• Abstract summary: We present a foundation modeling framework that leverages deep learning to uncover latent genetic signatures across the hematopoietic hierarchy.<n>Our approach trains a fully connected autoencoder on multipotent progenitor cells, reducing over 20,000 gene features to a 256-dimensional latent space.<n>We validate the quality of these embeddings by training feed-forward, transformer, and graph convolutional architectures for blood disease diagnosis tasks.<n>Our models achieve greater than 95% accuracy for multi-class classification, and in the zero-shot setting, we achieve greater than 0.7 F1-score on the binary classification task.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a foundation modeling framework that leverages deep learning to uncover latent genetic signatures across the hematopoietic hierarchy. Our approach trains a fully connected autoencoder on multipotent progenitor cells, reducing over 20,000 gene features to a 256-dimensional latent space that captures predictive information for both progenitor and downstream differentiated cells such as monocytes and lymphocytes. We validate the quality of these embeddings by training feed-forward, transformer, and graph convolutional architectures for blood disease diagnosis tasks. We also explore zero-shot prediction using a progenitor disease state classification model to classify downstream cell conditions. Our models achieve greater than 95% accuracy for multi-class classification, and in the zero-shot setting, we achieve greater than 0.7 F1-score on the binary classification task. Future work should improve embeddings further to increase robustness on lymphocyte classification specifically.

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