25 November 2021

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

Watch the replay


  • 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

Poll results

1. Talk 1: Machine learning will make translational research cheaper to conduct

Agree - 83%

Disagree - 17%


2. Talk 2: Approved drugs are highly specific, interacting with only one drug target in most cases.

Agree - 67%

Disagree - 33%


3. Talk 2: Modern computational and molecular interaction network-based drug development strategies offer clear advantages over conventional drug development

Agree - 96%

Disagree - 4%


4. Talk 3: AI approaches will reduce the time to a diagnosis of PAH within the next 5 years.

Agree - 79%

Disagree - 21%


5. Talk 4: Machine learning methodologies can discover well hidden intricate signals but are unable to ever uncover 100% of the truth behind the data.

Agree - 92%

Disagree - 8%


6. Talk 5: Should we stratify PAH patients based on gene expression profiles?

Yes - 79%

No - 21%

Our research platform is the world.

Through worldwide collaboration, we can begin to answer the question of a global disease.

Join the PVRI