Decoding cancer prognosis with deep learning: the ASD-cancer framework for tumor microenvironment analysis

Area of Science:

• Biomedical research
• Bioinformatics
• Artificial Intelligence in Oncology

Background:

• Deep learning advances multi-omics data integration in biomedical research.
• Classical bioinformatics benefits from AI by incorporating existing knowledge.
• Cancer research requires sophisticated tools for analyzing complex datasets.

Purpose of the Study:

• To introduce the Autoencoder-Based Subtypes Detector for Cancer (ASD-cancer) framework.
• To enhance multi-omics data analysis for cancer subtyping.
• To improve the scalability and performance of cancer data analysis.

Main Methods:

• Utilizing a semi-supervised learning framework (ASD-cancer).
• Employing autoencoders pre-trained on The Cancer Genome Atlas data.
• Leveraging transfer learning for processing new datasets without retraining.

Main Results:

• ASD-cancer demonstrates superior performance compared to baseline models.
• The framework exhibits scalability for processing large and new datasets.
• Pre-trained autoencoders improve multi-omics data analysis accuracy.

Conclusions:

• ASD-cancer offers a scalable and effective approach for cancer multi-omics data analysis.
• Future directions include integrating additional data layers and adaptive AI models.
• Incorporating large language models can enhance interpretability and insights in cancer subtyping.