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Single-Cell Transcriptomics and AI-Driven Cell State Classification

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 Metabolomic Pathway Analysis Using Unsupervised Learning Epigenetic Modification Prediction for Drug Sensitivity Assessment Microbiome Compositional Analysis and Pharmacogenomics Integration Temporal Omics Profiling with Recurrent Neural Networks

Ai Drug Repurposing › Machine Learning Omics-Driven Repurposing Studies

Single-Cell Transcriptomics and AI-Driven Cell State Classification

This research leverages machine learning to classify diseased cell states from single-cell RNA sequencing data and predict drug response at cellular resolution. The study generates cell-type-specific drug repurposing strategies that account for tissue heterogeneity and cellular context.

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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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