Clinical Responder Analysis | Patient Selection for Trials
Data Driven Clinical Insights
Patient & Clinician apps for safety and efficacy monitoring
Patient Generated Insights
250M Interactome Relationships For Network Analysis
Digital Twins for Clinical Trials
Finding Hidden Insights
BioXplor uses text analytics, machine learning and natural language processing to find hidden insights inside large aggregated datasets of structured and unstructured data in different formats from different public and private sources. Patient apps and Clinician dashboards are designed to capture insights on safety and efficacy during a clinical trial which can be used for monitoring and in post-trial analysis, as well as outside clinical trials, whereby the insights are added to BioXplor's core knowledge graph for deeper analysis. BioXplor's core knowledge graph combines over 250 million relationships between drugs, genes, variant, pathways, biological functions and processes, and patient demographics, bloods, adverse reactions, symptoms, and comorbidities by therapeutic area. Deeper analysis is performed using BioXplor's algorithms trained for patient responder and non-responder analysis, identification of patients and clinical sites for clinical trial selection, interpretation of clinical trials data during or after a trial, finding novel indications and drug combinations for existing drugs to match patient sub-groups, and recommending treatments for patients based on genomics and medical record insights. BioXplor's core knowledge graph can be accessed via a software visualization interface, while aggregated medical records, real-world evidence and clinical trials data can be ingested into the graph for deeper analysis via a secure, federated cloud-based integration.
"One of the biggest challenges in medicine and science today is INTERPRETATION of the vast amount of data being generated"