NTHRYS › Internships › Ai Cancer Biology › Machine Learning Cancer Biomarker Discovery Pipeline

Multi-Omics Integration via Graph Neural Networks

Ai Cancer Biology

Machine Learning Cancer Biomarker Discovery Pipeline

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Deep Learning Feature Extraction from Histopathology Images Temporal Sequence Modeling of Longitudinal Cancer Evolution Explainable AI for Clinical Biomarker Interpretation and Validation Adversarial Domain Adaptation for Cross-Cancer Biomarker Transfer Single-Cell RNA-Seq Analysis via Clustering and Trajectory Inference Transfer Learning from Natural Images to Cancer Image Classification Causal Inference and Bayesian Networks for Biomarker-Outcome Relationships Federated Learning for Privacy-Preserving Multi-Institutional Biomarker Discovery Active Learning and Uncertainty Quantification for Optimal Biomarker Sampling

Ai Cancer Biology › Machine Learning Cancer Biomarker Discovery Pipeline

Multi-Omics Integration via Graph Neural Networks

This study explores graph neural network frameworks that simultaneously integrate genomic, transcriptomic, proteomic, and metabolomic data to model cancer system biology. The research produces unified biomarker signatures that capture inter-omics dependencies and predict therapeutic vulnerability.

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📚 Academic: Thesis & PPT assistance included🧪 Tech: Master the protocols hands-on📝 Research > 3 months: Publication co-authorship in a Scopus-indexed journal

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