3. Data, Science & AI

Integrates cryo-EM structural data with generative AI and molecular dynamics for structure-based drug design targeting flexible proteins and membrane complexes.

AI-powered DNA methylation analysis for epigenomic profiling, differential detection, and cancer epigenome characterization using MethylGPT models.

AI-driven analysis of T-cell receptor repertoires using deep learning for cancer diagnosis, immunotherapy response prediction, and therapeutic TCR selection.

AI-driven detection and risk prediction for clonal hematopoiesis of indeterminate potential (CHIP) using genomic and clinical data.

AI-powered analysis of cancer metabolic reprogramming (Warburg effect, glutamine addiction, lipid metabolism) to identify therapeutic vulnerabilities.

AI-driven analysis of cellular senescence data to accelerate aging research, cancer therapy evaluation, and senolytic drug development.

AI-powered tool for calculating and optimizing polygenic risk scores across diverse ancestries to ensure equitable disease risk prediction globally.

AI-powered analysis of tumor heterogeneity using genomic sequencing to reconstruct clonal architecture, track evolution, and predict therapy resistance.

AI-driven analysis of cell-free RNA from liquid biopsies for non-invasive cancer detection and tissue-of-origin identification.

AI-powered integration of multi-omics datasets via probabilistic alignment for tissue atlas construction and cellular state mapping in biological research.

AI-driven prediction of optimal immune checkpoint inhibitor combinations using tumor microenvironment, biomarkers, and molecular profiling data.

AI-driven bone marrow morphology analysis, cell classification, and hematologic disorder diagnosis using deep learning on medical images.

AI-driven analysis of patient-derived xenograft models for drug response prediction and personalized treatment selection in oncology research.

Automates biomedical research workflows using AI, leveraging 150+ specialized tools and databases for data-driven scientific discovery.

Automates single-cell biological data analysis through an LLM-driven multi-agent framework.

AI-powered analysis of hemoglobin disorders using HPLC, electrophoresis, and molecular data for diagnostic support and research.

AI-driven tool optimizing NK cell therapy design for cancer immunotherapy, including CAR-NK engineering and KIR/HLA matching.

AI-powered analysis of minimal residual disease in multiple myeloma using next-generation flow cytometry, NGS, and mass spectrometry data.

AI agent using GPT-4 and vision transformers for cancer diagnosis, biomarker detection, and treatment planning in precision oncology.

AI agent implementing geometric deep learning (PRS-Net) for polygenic risk score prediction, modeling gene interactions with cross-ancestry disease prediction capabilities.

AI-driven analysis for coagulation disorders, thrombosis risk prediction, and anticoagulation management using machine learning models.

Toolkit connecting LLMs to biomedical data sources (PubMed, Genomics) via Model Context Protocol (MCP).

Creates AI-driven patient digital twins for clinical trial simulation, treatment outcome prediction, and personalized medicine using multi-omics and real-world data.

AI-powered analysis of single-cell RNA sequencing data to predict cellular dynamics, differentiation trajectories, and gene regulation patterns.