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Live Free Webinar on Leukocyte immunobiology helps us predict post-operative risk using pre-operative markers on 9 Aug, 8 am PST
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 long-standing 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.
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
Limited seats only. Register here to attend: http://www.strand-ngs.com/webinar_registration
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 long-standing 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.
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
Limited seats only. Register here to attend: http://www.strand-ngs.com/webinar_registration
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