BPRI Courses

1. Next-Gen Comparative Genomics Pipeline for Mpox

  • Sequence Alignment
  • End-to-end pipeline to fetch viral genomes from NCBI/GISAID and perform high-throughput alignment
  • Uses Python (Biopython) + Clustal Omega for multiple sequence alignment
  • Identifies conserved/mutated regions using statistical scoring
  • Visualization via Matplotlib/Seaborn dashboards
  • Deployable on Microsoft Azure for scalable genomic analysis

2. AI-Driven Pharmacovigilance Framework for Precision Therapeutics in Type 2 Diabetes

  • Integrates ADR datasets (FAERS) and patient clinical data
  • Uses ML models (Random Forest/XGBoost) to predict adverse drug responses
  • Personalized drug recommendation engine using classification pipelines
  • R (Bioconductor) for statistical validation
  • Cloud-based patient data storage + model deployment

3. Deep Learning-Based Prediction of Immune Escape Variants (SARS-CoV-2/Mpox)

  • Uses sequence datasets of SARS-CoV-2 and Mpox for mutation analysis
  • LSTM/Transformer models for sequence prediction
  • Predicts mutations impacting antibody binding
  • GPU-based training using TensorFlow/PyTorch
  • Deployable API for real-time mutation risk prediction

5. Integrative Multi-Omics Intelligence System for Cervical Cancer

  • Combines genomics, transcriptomics, proteomics datasets
  • Uses R (DESeq2) + Python (Pandas) for differential analysis
  • Identifies biomarkers and drug targets
  • Network analysis using Cytoscape
  • Dashboard visualization for clinical insights

7. Proteomics-Driven Drug Target Mapping in Cervical Cancer

  • Processes mass spectrometry proteomics datasets
  • Identifies differentially expressed proteins
  • Functional enrichment analysis using R tools
  • Links proteins to drug databases (DrugBank)
  • Visualizes pathways and targets

9. High-Throughput Genomic Variant Profiling in Cervical Cancer

  • Variant calling pipeline using GATK
  • SNP/Indel filtering using Python
  • Identifies oncogenic mutations
  • Integrates clinical datasets
  • Visual analytics dashboard

11. In-Silico Drug Discovery Pipeline for Mpox

  • Virtual screening of compounds (PubChem/ZINC)
  • Docking simulations using AutoDock
  • Ranking based on binding energy
  • ADMET prediction using ML models
  • Cloud-based HPC screening

13. AI-Powered Antimicrobial Resistance Prediction

  • Genome analysis for resistance genes
  • ML classification models
  • Phylogenetic tree construction
  • Data pipelines in Python
  • Cloud deployment for surveillance

15. Gut Microbiome Metagenomic Intelligence Platform

  • QIIME2 pipeline for sequencing data
  • Diversity analysis (alpha/beta diversity)
  • Clustering using ML algorithms
  • Correlation with immune diseases
  • Cloud workflow automation

17. Computational Epigenomics Platform

  • “Next-Gen Epigenomic Intelligence System for Cardiovascular Disease Risk Profiling”
  • DNA methylation and histone modification analysis
  • R-based statistical modeling (Bioconductor)
  • Integration with transcriptomics data
  • AI models to predict disease risk
  • Interactive visualization dashboards

19. Drug Interaction Prediction Engine

  • “Graph AI-Based Drug-Drug Interaction Prediction Platform for Precision Medicine”
  • Builds drug interaction networks using graph models
  • Applies ML classification for interaction prediction
  • Integrates DrugBank datasets
  • API-based prediction service
  • Dashboard for clinical decision support

21. Epitranscriptomics Intelligence System

  • “RNA Modification Analytics Platform for Decoding Cancer Progression via Epitranscriptomics”
  • Detects RNA modifications (m6A, m5C)
  • Integrates RNA-seq datasets
  • Correlates modifications with cancer stages
  • AI models for predictive insights
  • Visualization of RNA modification landscapes

23. Drug Response Variability Platform

  • “Multi-Omics AI Engine for Predicting Drug Response Variability in Oncology”
  • Integrates genomic + epigenomic data
  • Uses ML models for prediction
  • Identifies key regulators
  • Personalized therapy recommendations
  • Visualization dashboards

25. Mpox CADD Platform

  • “Advanced AI-Driven CADD Pipeline for Novel Therapeutic Target Discovery Against Mpox”
  • Identifies viral targets via structural analysis
  • Virtual screening + docking simulations
  • ML-based scoring of candidates
  • ADMET optimization
  • Cloud HPC integration

27. Ligand Screening Engine

  • “AI-Powered Ligand-Based Virtual Screening Platform for Viral Entry Inhibitor Discovery”
  • QSAR modeling for compound activity prediction
  • Chemical descriptor extraction using Python
  • ML-based screening of libraries
  • Ranking of top inhibitors
  • Validation pipeline

29. Mpox Drug Discovery Platform

  • “End-to-End AI-Enabled Drug Discovery System for High-Efficacy Mpox Therapeutics”
  • Compound library screening
  • Docking + ADMET analysis
  • ML-based ranking
  • Optimization of drug properties
  • Cloud deployment

31. Cancer Hub Gene Engine

  • “Integrated Bioinformatics Platform for Hub Gene Discovery and Drug Repurposing in Cancer”
  • Gene expression analysis
  • Network construction (Cytoscape)
  • Hub gene identification
  • Drug repurposing via databases
  • Visualization dashboards

33. Pan-Genome Intelligence Platform

  • “Pan-Genome Analytics System for Antibiotic Resistance Profiling in Klebsiella pneumoniae”
  • Genome collection and comparison
  • Core/accessory gene identification
  • Resistance gene profiling
  • Phylogenetic analysis
  • Visualization tools

35. Herbal Drug Discovery Platform

  • “Computational Metabolomics Engine for Bioactive Compound Discovery in Herbal Medicine”
  • Metabolite database mining
  • Docking analysis
  • ML-based activity prediction
  • Identification of bioactive compounds
  • Visualization

37. Rheumatoid Arthritis Simulation Engine

  • “Systems Biology-Based Computational Engine for Modeling Inflammatory Pathways in Rheumatoid Arthritis”
  • Pathway modeling using ODEs
  • Simulation of inflammation dynamics
  • System-level predictions
  • Validation using datasets
  • Visualization

39. scRNA-seq Automation Platform

  • “End-to-End AI-Driven Workflow Automation System for Single-Cell RNA-Seq Analysis”
  • QC and filtering pipeline
  • Normalization and clustering
  • Cell type annotation
  • Automation scripts
  • Visualization

41. Gene-Disease Network Platform

  • “Graph-Based Gene-Disease Network Analytics Engine for Rare Mitochondrial Disorders”
  • Network construction
  • Graph algorithms
  • Key gene identification
  • Visualization tools
  • Predictive insights

43. GNN Cancer Intelligence Platform

  • “Graph Neural Network-Based Gene Regulatory Network Inference Platform for Cancer Systems Biology”
  • Builds gene regulatory networks
  • Applies GNN models
  • Infers gene interactions
  • Cancer pathway analysis
  • Visualization dashboards

4. Structure-Based Precision Drug Discovery for Alzheimer’s via Docking

  • Targets Alzheimer’s disease proteins (Amyloid-beta)
  • Uses AutoDock Vina for ligand-protein docking
  • RDKit for chemical optimization and ADMET prediction
  • AI-based ranking of compounds using regression models
  • Cloud HPC for large-scale docking simulations

6. Network Pharmacology Framework for miR-4454 Target Discovery

  • Extracts miRNA-gene interactions from databases
  • Builds gene-drug interaction networks
  • Pathway enrichment using KEGG/GO analysis
  • Uses Python (NetworkX) for graph modeling
  • Identifies druggable targets via network centrality

8. AI-Augmented Variant Annotation in Breast Cancer

  • Uses TCGA mutation datasets
  • Variant calling and annotation via ANNOVAR
  • ML model to classify pathogenic mutations
  • Python pipelines for automation
  • Cloud storage + compute integration

10. Genome-Wide Analysis of Type 2 Diabetes

  • GWAS data processing
  • Identifies gene-disease associations
  • Uses R for statistical modeling
  • ML prediction of risk genes
  • Network visualization

12. Hybrid Sequence Alignment & Homology Modeling Platform

  • BLAST for sequence similarity search
  • Clustal Omega for alignment
  • SWISS-MODEL for 3D structure prediction
  • Structure validation tools
  • Visualization using PyMOL

13. AI-Powered Antimicrobial Resistance Prediction

  • Genome analysis for resistance genes
  • ML classification models
  • Phylogenetic tree construction
  • Data pipelines in Python
  • Cloud deployment for surveillance

16. CRISPR Functional Genomics Engine

  • “AI-Optimized CRISPR Genome Engineering Platform for Precision Therapeutics in Rare Genetic Disorders”
  • Designs high-efficiency gRNA with off-target minimization
  • Uses Python pipelines + CRISPR design tools
  • ML-based scoring for editing efficiency
  • Simulates gene-edit outcomes in silico
  • Cloud deployment for scalable genome editing workflows

18. Single-Cell Tumor Intelligence Platform

  • “AI-Powered Single-Cell Analytics Engine for Tumor Microenvironment Deconvolution”
  • scRNA-seq processing using Seurat
  • Cell clustering + annotation via ML
  • UMAP/t-SNE for visualization
  • Detects tumor heterogeneity patterns
  • Cloud-enabled large-scale cell analysis

20. CKD Biomarker Discovery Platform

  • “AI-Driven Early Detection System for Chronic Kidney Disease via Genomic Biomarkers”
  • Gene expression analysis for biomarker discovery
  • ML classification for early-stage detection
  • Statistical validation using R
  • Predictive modeling for risk assessment
  • Clinical dashboard visualization

22. Protein Interaction Modeling Engine

  • “Structural Bioinformatics Platform for Protein-Protein Interaction Mapping in Neurodegenerative Diseases”
  • Builds PPI networks using STRING DB
  • Identifies hub proteins via graph analysis
  • Structural modeling of interactions
  • Cytoscape-based visualization
  • AI-assisted target prioritization

24. Circular RNA Cancer Platform

  • “AI-Based Circular RNA Regulatory Network Platform for Cancer Metastasis Prediction”
  • Detects circRNAs from RNA-seq
  • Builds regulatory interaction networks
  • Functional prediction via ML
  • Identifies metastasis biomarkers
  • Visualization tools

26. PTM Analysis Platform

  • “Post-Translational Modification Intelligence System for Disease Proteomics”
  • Identifies phosphorylation/acetylation sites
  • Correlates PTMs with disease states
  • ML prediction of modification sites
  • Protein function analysis
  • Visualization of PTM patterns

28. Structure-Based Drug Design Suite

  • “Next-Gen Structure-Based Drug Design Platform for Viral Protease Inhibitor Optimization”
  • Protein structure modeling and docking
  • Ligand optimization pipelines
  • Binding affinity analysis
  • AI-driven compound refinement
  • Visualization of molecular interactions

30. Multi-Target Anti-Cancer Platform

  • “AI-Integrated Multi-Target Drug Discovery Engine with Docking and Quantum (DFT) Analysis”
  • Identifies multiple cancer targets
  • Docking simulations across targets
  • DFT analysis for compound stability
  • Optimization pipelines
  • Visualization of results

32. Personalized Nutrition AI System

  • “SNP-Driven Precision Nutrition Recommendation Engine Using Gut Microbiome Analytics”
  • SNP data analysis
  • Microbiome correlation
  • ML-based diet recommendations
  • Personalized health insights
  • API deployment

34. CRISPR Crop Engineering Platform

  • “AI-Optimized CRISPR gRNA Design System for Enhancing Stress Tolerance in Rice”
  • Target gene identification
  • gRNA design and scoring
  • Off-target prediction
  • Simulation of edits
  • Optimization pipeline

36. Skin Microbiome Intelligence System

  • “Comparative Metagenomic Platform for Skin Microbiome Profiling in Psoriasis and Eczema”
  • Metagenomic sequencing analysis
  • Diversity comparison
  • Statistical modeling
  • Disease correlation
  • Visualization dashboards

38. QSAR Anti-TB Platform

  • “Automated AI-Powered QSAR Modeling Platform for Anti-Tuberculosis Drug Discovery”
  • Dataset preparation
  • Descriptor extraction
  • ML model training
  • Prediction of activity
  • Validation

40. Neoantigen Discovery Platform (Glioblastoma)

  • “AI-Powered Neoantigen Discovery Engine for Personalized Immunotherapy in Glioblastoma”
  • Tumor mutation analysis
  • Neoantigen prediction
  • Immunogenicity scoring
  • ML-based ranking
  • Visualization

42. AI Cancer Vaccine Platform

  • “Deep Learning-Based Neoantigen Prediction Platform for Personalized Cancer Vaccine Design”
  • Tumor sequencing analysis
  • Deep learning models
  • Antigen prediction
  • Ranking candidates
  • Deployment
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