ChatGPT Meets Drug Discovery: Unlocking New Frontiers
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CASE STUDY
Predicting Survivability of Patients with Liver Hepatocellular Carcinoma
Key Highlights
Hepatocellular cancer has a high mortality rate but making a prognosis is challenging.
Predicting the survival outcomes of liver cancer patients using a single type of biomedical molecular data is challenging.
Multi-omics data from Liver OmixAtlas data can be integrated to train ML models for predicting patient survivability.
Our ML model, trained on integrated multi-omics data, could predict liver cancer patient survivability with a high level of precision.
All Case Studies
Oncology Company Achieves ~80% Acceleration in Gene Target ID/ Validation
Pharma-AI Collaboration Drives ~$3M Cost Reduction by Using Highly Curated Public Data
Leading Oncology Company Uses Elucidata’s Drug OmixAtlas to Drive Drug Discovery
Polly PeakML Accelerates Biological Insight Derivation from Untargeted Metabolomics Analysis
Cutting- Edge Cancer Tx Accelerated Target ID by 75% using ML & Curated Biomolecular Data
Cloud Processing of Metabolomics Data
Accelerate Cell-Type Annotation of scRNA-seq Data
Predicting Survivability of Patients with Liver Hepatocellular Carcinoma
Integrated Metabolomics and Transcriptomics Profiling
Network Integration of Parallel Metabolic and Transcriptional Data Reveals Metabolic Modules that Regulate Macrophage Polarization
Why Polly CRISPR Screening is Better than MAGeCK VISPR
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