Discover how we collaborated with the UroCCR network to develop a machine learning model that outperformed the predictive performance of most usual kidney cancer risk scores.
We’re often asked how we avoid overfitting when developing predictive machine learning models for clinical research. This technical note explains how.
Capturing the complexity of human health and disease through machine learning analysis of multimodal data has the potential to drive the future of healthcare.
Liquid biopsy is revolutionizing precision oncology with a non-invasive approach to tumor profiling. In this blog, we explore its clinical applications and why the right analytical technologies are needed when searching in a sea of genomic data.
Despite the advantages of cascade testing, it is a vastly underused cancer prevention strategy. What are the barriers and facilitators for cascade testing in hereditary cancer syndromes?
Companion Diagnostics (CDx) are important tools in the practice and application of precision medicine. This introductory blog provides an overview of their development through clinical trials, regulatory implications, and clinical utility.
Deep learning-based approaches to genomic instability assessment can help overcome the limitations of current methods and maximize insights from tumor samples.
This guide deciphers the jargon associated with machine learning in healthcare and explains why artificial intelligence is invaluable to revolutionize the capabilities of HCPs in improving patient care.
Hospices Civils de Lyon in France share their insights from 5 years of experience using NGS for the molecular diagnosis of inherited cardiac diseases.
Learn how we addressed the unique challenges associated with genotyping mitochondrial DNA (mtDNA) to develop robust pipelines for mitochondrial genome analysis.
Read our descriptive guide to the HGVS nomenclature standard, genome reference assemblies, and MANE transcripts and how they are applied in Alamut™️ Visual Plus in this Technical Note.
Our platform has analyzed over 1 million genomic profiles, we chatted with some experts to see how this data is helping to improve our knowledge over time.
