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

Explainable AI for Clinical Biomarker Interpretation and Validation

Ai Cancer Biology

Machine Learning Cancer Biomarker Discovery Pipeline

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Deep Learning Feature Extraction from Histopathology Images Multi-Omics Integration via Graph Neural Networks Temporal Sequence Modeling of Longitudinal Cancer Evolution 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

Explainable AI for Clinical Biomarker Interpretation and Validation

This research develops SHAP, LIME, and attention-based interpretability methods to deconstruct machine learning predictions into clinically actionable biomarker components. The work bridges computational discovery and clinical translation by validating which learned features correspond to biologically meaningful cancer signatures.

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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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