3. Data, Science & AI

Predicts potential off-target binding sites for CRISPR-Cas9 guide RNAs by analyzing sequence mismatches to improve gene editing accuracy.

ML framework inferring high-risk clonal hematopoiesis from routine blood tests, minimizing unnecessary genomic sequencing for CHIP screening.

Predicts pathogenicity of genetic variants using PopEVE deep learning model for population-aware disease variant identification and rare disease diagnosis.

Analyzes spatial transcriptomics data using foundation models to model cellular architecture and discover tissue niches from 53M+ spatially resolved cells.

AI-driven pharmacogenomic analysis for predicting drug responses, assessing adverse events, and optimizing dosing using multi-omics data and deep learning.

AI-powered analysis of circulating tumor DNA dynamics for molecular residual disease detection, treatment monitoring, and relapse prediction using liquid biopsy.

AI-driven design of targeted gene panels for clinical diagnostics and research, supporting cancer, pharmacogenomics, and rare disease applications.

AI-powered integration of chromatin accessibility, histone modifications, and DNA methylation data with spatial coordinates for tissue architecture mapping.

AI analysis of T-cell exhaustion states, epigenetic scarring, and checkpoint blockade response prediction for cancer immunotherapy research.

AI-powered analysis for predicting, monitoring, and managing cytokine release syndrome (CRS) in immunotherapy and infectious disease contexts.

AI-driven analysis of patient-derived organoid drug screening data to enable personalized cancer treatment selection and biomarker discovery.

AI-driven integration of genomic, transcriptomic, proteomic, and epigenomic data for cancer subtyping, driver gene identification, and cross-cancer pattern discovery.

AI-driven fusion of radiology, pathology, and genomic data for comprehensive cancer diagnostics and treatment prediction.

AI-driven prediction of TCR-peptide-MHC interactions using AlphaFold3 and deep learning for therapeutic TCR discovery and neoantigen validation.

AI-powered immune profiling of tumor microenvironments using bulk deconvolution, single-cell analysis, and spatial transcriptomics for immunotherapy biomarker discovery.

AI-driven analysis of genomic scarring signatures and BRCA pathway to predict PARP inhibitor response in cancer treatment.

AI-driven analysis of exosomes and extracellular vesicles for cancer biomarker discovery, liquid biopsy applications, and intercellular communication profiling.

AI-driven fusion of radiology imaging, digital pathology, genomics, and clinical data for comprehensive cancer diagnosis and treatment planning.

AI-powered design of armored CAR-T cells with cytokine expression to enhance solid tumor treatment efficacy.

AI-powered analysis of tumor clonal architecture and subclonal dynamics from multi-region sequencing and liquid biopsy data.

AI-powered analysis of liquid biopsy biomarkers for cancer detection, monitoring, and treatment guidance using ctDNA, CTCs, and bioinformatics.

AI-driven prediction of ternary protein complexes to optimize PROTAC and molecular glue drug efficacy in targeted protein degradation.

AI-driven tool for designing adeno-associated virus vectors in gene therapy, optimizing capsid structure, promoter selection, and cellular targeting.

AI-powered analysis of chromosomal instability signatures for cancer prognosis, immunotherapy response prediction, and therapeutic vulnerability identification.