Machine learning and artificial intelligence approaches for PH: From screening to novel drug targets

24 November 2021

  • days
  • Hours
  • Minutes

Chair and moderators

Mark Gladwin, University of Pittsburgh Medical Centre, USA

Martin Wilkins, Imperial College London, UK

Raymond Benza, Ohio State University, USA

Agenda

  • Dennis Wang // Looking beyond the hype: Applied AI and machine learning in translational medicine
  • Joseph Loscalzo // Computational network pharmacology in cardiovascular disease – leveraging big data
  • David Kiely // Utilizing artificial intelligence to screen and diagnose iPAH
  • Sokratis Kariotis // Biological heterogeneity in idiopathic pulmonary arterial hypertension identified through unsupervised transcriptomic profiling of whole blood
  • Yajing Ji // Transcriptomic characterization of Pulmonary arterial hypertension on pathway level

A deeper look

Machine learning and artificial intelligence has the potential to shift clinical research and healthcare by providing novel insight into phenotyping, predictive and diagnostic capabilities, and screening and identification of novel therapeutic targets. Artificial intelligence generally refers to a collection of algorithms that perform tasks that would normally be done by humans. Machine learning algorithms are typically split into two categories: supervised and unsupervised learning. Unsupervised algorithms will perform classification tasks based on unlabeled datasets. Supervised algorithms require labeled datasets to train the algorithm to perform correct classifications. Leveraging big data and machine learning has the potential to shed light on the heterogeneity in pulmonary hypertension (PH).

“The machine learning and artificial intelligence approaches for PH” webinar focuses on novel applications of machine learning and artificial intelligence that are pushing PH research forward. The first talk by Dennis Wang (University of Sheffield, UK) will provide an overview of the application of AI and machine learning in translational medicine. The second presentation by Joseph Loscalzo (Harvard University, USA) will discuss leveraging big data and computational network pharmacology in cardiovascular disease settings. The third presentation by David Kiely (University of Sheffield, UK) focuses on utilizing artificial intelligence as a means to screen and diagnose pulmonary arterial hypertension. The session will end with two best abstract presentations focusing on transcriptomic phenotyping in PH. Sokratis Kariotis (University of Sheffield, UK) will present on the identification of biological heterogeneity in PAH through unsupervised transcriptomic profiling of whole blood. Yajing Ji (Michigan State University, USA) will discuss pathway level  transcriptomic characterization of PAH. 

Machine learning and artificial intelligence approaches in cardiovascular and pulmonary settings have potential to assist in diagnostics, identification of therapeutic and drug targets, and assessment of phenotypic and genotypic heterogeneity in PH. These advancements can help develop better understanding of the pathophysiology of PH.

 

Looking beyond the hype: Applied AI and machine learning in translational medicine

15:00-15:15 GMT

Presenter: // Dennis Wang, University of Sheffield, UK

 

Computational network pharmacology in cardiovascular disease – leveraging big data

15:27-15:42 GMT

Presenter: // Joseph Loscalzo, Harvard University, USA

 

Utilizing artificial intelligence to screen and diagnose iPAH

15:54-16:09 GMT

Presenter: // David Kiely, University of Sheffield, UK

 

Biological heterogeneity in idiopathic pulmonary arterial hypertension identified through unsupervised transcriptomic profiling of whole blood

16:21-16:31 GMT

Presenter: // Sokratis Kariotis, University of Sheffield, UK

 

Transcriptomic characterization of Pulmonary arterial hypertension on pathway level

16:41-16:51 GMT

Presenter: // Yajing Ji, Michigan State University, USA

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