Live Webinar: Integration of NGS and Machine Learning for prediction of post-operative recovery

Webinar on Integration of NGS and Machine Learning for prediction of post-operative recovery on 9 Aug, 8 am PST

About Speaker: 
Mario Deng MD FACC FESC , Professor of Medicine, Advanced Heart Failure/Mechanical Support/Heart Transplant, David Geffen School of Medicine at UCLA, Ronald Reagan UCLA Medical Center

Abstract:
Strand NGS supports a comprehensive and flexible RNA-Seq data analysis workflow consisting of Alignment, Quality Assessment, Filters, and a range of analysis and visualization options that help in studying a variety of samples and answering important biological questions.

In this webinar, Dr. Deng will discuss the analysis of transcriptome, flow cytometry and cytokine data from pre-operative blood samples of advanced heart failure patients undergoing Mechanical Circulatory Support (MCS) surgery. He will discuss in detail the identification of prominent clinical variables, a set of transcriptome biomarkers, and their role in the context of systems biology. Finally, the application of Class Prediction algorithms in Strand NGS for identification of high-risk patients will be illustrated.

This immunobiology based study highlights the potential of machine learning techniques in clinical risk prediction and patient management, and from a clinician’ s perspective, the utility of biomarker discovery studies in helping patients make more informed decisions as a step towards personalized precision medicine.

To attend, register here

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