Dan is the Chief Data Officer at InterVenn, with over 15 years of experience mining the genome and proteome for biological and clinical insights. After joining in late 2017, his team was responsible for training, deploying, and publishing the worlds first neural network for mass spectrometry peak selection and integration. Prior to starting at InterVenn, Dan led analytical efforts for dozens of scientific publications in cancer, neurodegenerative disease, and cardiovascular health at the Mayo Clinic. He holds a Bachelor of Science degree in Mathematics from the University of Minnesota and a Masters in Applied Statistics from Penn State. His team continues development on advanced machine-learning techniques for detecting, quantifying, and utilizing glycoproteins, in service of the ultimate goal: moving these novel biomarkers into clinical practice and improving patient outcomes.
Clinical Dx Showcase:
Glycoproteomics: Next-Gen Biomarkers in Personalized Health Care
Recent advances in analytical technology, based on a combination of massspectrometry and artificial intelligence, have opened up a vast new territory of powerful biomarkers, orders of magnitude richer and more sensitive and specific than genomics and proteomics: posttranslational modification moieties of proteins, specifically glycosylation isoforms, as demonstrated by InterVenns data across a range of indications.
The PMWC 2023 Data Applications in Clinical Diagnostics Showcase will provide a 15-minute time slot for selected organizations, including commercial companies, clinical testing labs, and medical research institutions, to present their latest advancements, insights, applications, and technologies to an audience of clinicians, leading investigators, academic institutions, pharma and biotech, investors, and potential clients. We will learn about new technologies and findings that promise expedited, cost-effective, and accurate clinical diagnosis for early disease detection, treatment decisions, and disease prevention.