Dr. Jesus Rodriguez Manzano
Lecturer in AMR and Infectious Diseases, Imperial College London
Jesus Rodriguez Manzano holds a BSc in Biological Sciences, an MSc in Advanced Microbiology, and a PhD in Microbiology and Biotechnology, all earned from the University of Barcelona, Spain. Following his doctoral studies, he pursued post-doctoral positions in esteemed institutions, including the Division of Chemistry and Chemical Engineering at the California Institute of Technology (Caltech) in the United States and the Department of Electrical and Electronic Engineering at Imperial College London in the United Kingdom. Dr. Rodriguez Manzano's expertise led him to his current role as a Lecturer (Assistant Professor) in the Department of Infectious Disease at Imperial College London, where he contributes to advancing the field of Molecular Diagnostics to tackle infection and antimicrobial resistance.
Next-Generation Molecular Diagnostics: Leveraging Digital Technologies To Enhance Multiplexing in Real-Time PCR
segregation of the sample enables parallel amplification of multiple targets.
There is a need for innovations that will push forward the multiplexing field in qPCR, enabling for the next generation of diagnostic tools which could accommodate high throughput in an affordable manner. To this end, the use of machine learning algorithms has recently emerged to leverage information contained in amplification and melting curves– two of the most standard biosignals emitted during qPCR – for accurate classification of multiple nucleic acid targets in a single reaction.
Thermo Fisher Scientific are not affiliated with the webinar speaker. Any application of machine learning technologies with the Diomni™ software package, related assay definition files (ADFs) and related qPCR instruments are for research use only.
Next-Generation Molecular Diagnostics: Leveraging Digital Technologies To Enhance Multiplexing in Real-Time PCR
segregation of the sample enables parallel amplification of multiple targets.
There is a need for innovations that will push forward the multiplexing field in qPCR, enabling for the next generation of diagnostic tools which could accommodate high throughput in an affordable manner. To this end, the use of machine learning algorithms has recently emerged to leverage information contained in amplification and melting curves– two of the most standard biosignals emitted during qPCR – for accurate classification of multiple nucleic acid targets in a single reaction.
Thermo Fisher Scientific are not affiliated with the webinar speaker. Any application of machine learning technologies with the Diomni™ software package, related assay definition files (ADFs) and related qPCR instruments are for research use only.
