Showcasing our expertise through demo projects, anonymized outputs, and publicly available examples across bioinformatics, AI, analytics, and training.
Demonstration docking workflow using publicly available protein structures (PDB ID: 1M17) and ligand libraries. Includes binding affinity prediction, pose analysis, and 2D/3D visualization.
Anonymized MD simulation of a representative protein-ligand complex (100 ns trajectory). Analysis includes RMSD, RMSF, radius of gyration, and hydrogen bond occupancy using public tutorial datasets.
Structure-based virtual screening on a target protein (publicly available from ZINC database). Demonstrates hit identification, ADMET prediction, and lead optimization strategy.
Publicly available dataset (CIFAR-10/MNIST) used for training a convolutional neural network achieving 92% accuracy. Complete pipeline: preprocessing, augmentation, training, and evaluation.
Anonymized clinical dataset (publicly available from UCI repository) used to predict patient outcomes. Feature importance, model tuning, and performance metrics (AUC, F1-score).
Demo RNA-seq differential expression analysis using public dataset (GEO). Includes quality control, read alignment, count normalization, DEG identification, and pathway enrichment.
Public survey data (e.g., World Bank open data) analyzed using multiple linear regression, logistic regression, and correlation matrices. Complete interpretation report included.
Anonymized interview transcripts analyzed using NVivo. Demonstrates thematic coding, node hierarchy, and visualizations (word clouds, charts) for qualitative reporting.
Interactive dashboards using public economic/financial datasets (e.g., IMF, Kaggle). Includes KPI tracking, forecasting, and executive summary reports.
Customized bar charts, line graphs, and scatter plots for research manuscripts.
3D molecular renderings with PyMOL and Chimera.
High-resolution heatmaps for omics and survey data.
Confusion matrices, ROC curves, and SHAP summary plots.
Live online training covering sequence analysis, docking, and MD simulation. Includes session recordings, assignments, and feedback highlights.
Intensive 4-week program for researchers. Participant projects included real-world survey analysis and qualitative coding.
University collaboration: 30+ graduate students trained in machine learning for biomedical applications.
All examples use demo projects, publicly available datasets, or anonymized outputs. No confidential client data is shared.
