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Machine Learning Omics-Driven Repurposing Studies

Ai Drug Repurposing

Machine Learning Omics-Driven Repurposing Studies

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Machine Learning Omics-Driven Repurposing Studies

Internship matching drug-induced omics signatures against disease signatures to nominate reversals. Hands-on work runs alongside theory modules.

The focused areas below are internship topics in varied working formats. Pick one, then choose your internship type, mode… Read more

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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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Showing 1–10 of 10

Deep Learning Protein Structure Prediction for Drug Binding

This research investigates how deep neural networks can predict three-dimensional protein conformations and identify novel binding pockets for existing pharmaceuticals. The study produces validated computational models that accelerate the discovery of new therapeutic targets and drug-protein interactions without experimental synthesis.

Academic (A)Tech (T)Research (R)

14 more durationsView Titles →

Genomic Variant Interpretation Using Machine Learning Models

This research examines machine learning algorithms that classify genetic variations and predict disease susceptibility to identify patient populations responsive to repurposed drugs. The study generates predictive biomarkers that enable precision medicine approaches for patient stratification and personalized drug repositioning strategies.

Academic (A)Tech (T)Research (R)

14 more durationsView Titles →

Transcriptomic Signature Matching for Disease-Drug Correlation

This research explores how gene expression profiling combined with machine learning can match disease transcriptomes to drug-induced expression changes in databases. The study produces novel disease-drug associations that reveal unexpected therapeutic opportunities through molecular signature alignment.

Academic (A)Tech (T)Research (R)

14 more durationsView Titles →

Multi-Modal Omics Integration via Graph Neural Networks

This research develops graph-based deep learning architectures that integrate genomics, proteomics, and metabolomics data to identify drug repurposing candidates. The study yields systems-level insights into how drugs modulate biological networks and predict polypharmacology effects across multiple molecular layers.

Academic (A)Tech (T)Research (R)

14 more durationsView Titles →

Protein-Protein Interaction Networks for Target Discovery

This research applies machine learning to map protein interaction networks and identify secondary drug targets for existing compounds using omics databases. The study produces a comprehensive understanding of drug pleiotropy and reveals alternative therapeutic pathways for repurposed medications.

Academic (A)Tech (T)Research (R)

14 more durationsView Titles →

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.

Academic (A)Tech (T)Research (R)

14 more durationsView Titles →

Metabolomic Pathway Analysis Using Unsupervised Learning

This research employs clustering and dimensionality reduction algorithms to identify metabolic dysregulation patterns and match them to drug-induced metabolic corrections. The study produces mechanistic insights into how repurposed drugs restore metabolic homeostasis in disease states.

Academic (A)Tech (T)Research (R)

14 more durationsView Titles →

Epigenetic Modification Prediction for Drug Sensitivity Assessment

This research uses machine learning models trained on epigenomic datasets to predict how DNA methylation and histone modifications influence drug efficacy and response. The study reveals epigenetic biomarkers that identify patient subgroups most likely to benefit from specific repurposed therapeutics.

Academic (A)Tech (T)Research (R)

14 more durationsView Titles →

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.

Academic (A)Tech (T)Research (R)

14 more durationsView Titles →

Temporal Omics Profiling with Recurrent Neural Networks

This research develops recurrent neural networks and sequence models to analyze time-series omics data and predict dynamic disease-drug interactions. The study produces kinetic models of how repurposed drugs modify biological trajectories and disease progression rates over time.

Academic (A)Tech (T)Research (R)

14 more durationsView Titles →