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Microbiome Compositional Analysis and Pharmacogenomics Integration

Ai Drug Repurposing

Machine Learning Omics-Driven Repurposing Studies

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Deep Learning Protein Structure Prediction for Drug Binding Genomic Variant Interpretation Using Machine Learning Models Transcriptomic Signature Matching for Disease-Drug Correlation Multi-Modal Omics Integration via Graph Neural Networks Protein-Protein Interaction Networks for Target Discovery Single-Cell Transcriptomics and AI-Driven Cell State Classification Metabolomic Pathway Analysis Using Unsupervised Learning Epigenetic Modification Prediction for Drug Sensitivity Assessment Temporal Omics Profiling with Recurrent Neural Networks

Ai Drug Repurposing › Machine Learning Omics-Driven Repurposing Studies

Microbiome Compositional Analysis and Pharmacogenomics Integration

This research applies machine learning to correlate gut microbiome composition with drug metabolism and therapeutic outcomes for repurposing studies. The study establishes microbiome-based predictive models that optimize drug selection based on patient-specific microbial 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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