Neil Versel
Neil Versel is an editor at GenomeWeb. He covers the life science and healthcare informatics markets.

Articles Authored by Neil Versel
The company has committed at least $289 million to a partnership with Google Cloud to accelerate AI and other informatics.
The company, rebranded as Gedi Cube just two months ago, will become Renovaro.AI as it seeks to marry multiomics data and medical imaging in pursuit of faster cancer diagnoses and treatments.
A month into a collaboration with Nashville Biosciences, the Korean company is looking for additional partnerships with research institutions and Big Pharma to accelerate target discovery.
Building off ChatGPT Popularity, Generative AI Starts Finding its Place in Genome Informatics
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Bioinformaticians, researchers, and commercial software developers worldwide are trying to harness the power of generative AI for genomics without compromising scientific integrity.

The Swedish bioinformatics firm is targeting February 2025 for regulatory approval of its Qlucore Diagnostics software for transcriptomics-based ALL testing.
With Tempus Test Integration, CureMD Wants Genomics to Inform Oncology Clinical Decision Support
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The specialty EHR vendor is building a two-way interface with Tempus as a model for incorporating genomic test ordering and results return into oncology workflows.
Initial results from a multinational rapid WGS newborn screening program are promising, though organizers hope to create a longitudinal study to support potential clinical uptake.
Startup Gero to Advance Epigenetic Biomarker Discovery Through Foxo Technologies Partnership
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As Gero moves its operations from Russia and Singapore to the US, partner Foxo will provide longitudinal DNA methylation data to inform AI and validate recent research.
The Switzerland-based startup is also developing multiomics visualization and interpretation software to support drug discovery and biomarker ID.
Researchers Find Gene-Specific Machine Learning Advantages for BRCA Pathogenicity Prediction
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Two separate papers from Korea and Qatar delved into using gene-specific machine-learning models versus disease-specific and genome-wide machine-learning methods for predicting pathogenicity.