Labsent
The AI-driven research engine for scientific literature synthesis and lab protocol automation.
Accelerating precision medicine through single-cell genomics and machine learning-driven drug discovery.
Celsius Therapeutics leverages its proprietary SCOPE (Single-cell Multi-omics Precision Engine) platform to redefine drug discovery for complex immune-mediated diseases and oncology. By integrating high-resolution single-cell RNA sequencing (scRNA-seq) with advanced machine learning algorithms, the platform identifies the specific cellular drivers of disease within complex tissue ecosystems. Unlike traditional bulk sequencing, which averages signals across different cell types, Celsius's architecture maintains the granular resolution of individual cells, allowing for the identification of rare cell populations and specific gene expression pathways that drive pathology. By 2026, the platform has matured into a benchmark for patient stratification, enabling the discovery of first-in-class therapeutic targets and biomarkers that predict patient response with high fidelity. The technical stack combines cloud-based bioinformatics pipelines, deep learning models for cell-state transition mapping, and longitudinal clinical data integration to transform observational biology into actionable therapeutic programs. This approach positions Celsius as a critical partner for major pharmaceutical entities looking to de-risk clinical development through molecularly defined patient cohorts.
Scalable Cell-Omics Precision Engine capable of processing millions of individual cells across diverse patient cohorts.
The AI-driven research engine for scientific literature synthesis and lab protocol automation.
AI-driven neoantigen discovery and self-amplifying mRNA vaccines for precision oncology and infectious diseases.
Accelerating precision medicine through AI-powered metagenomics and novel gene-editing systems.
AI-Powered Medical Insights Platform for Life Sciences and Medical Affairs.
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ML algorithms that compare cellular signatures across different tissues to find common disease drivers.
Correlates single-cell data with temporal clinical outcomes to identify predictors of drug resistance.
An extensive library of curated single-cell data from IBD and oncology patients.
Dynamic unsupervised learning models that discover new cell types without prior labeling.
In silico modeling of how specific drug candidates affect gene regulatory networks.
Interactive UMAP and t-SNE interfaces for navigating complex cellular landscapes.
Identifying why certain patients with ulcerative colitis do not respond to anti-TNF therapy.
Registry Updated:2/7/2026
Predicting which patients will benefit from immune checkpoint inhibitors.
Understanding the cellular heterogeneity in Lupus.