Machine learning and artificial intelligence approaches for PH: From screening to novel drug targets
A PVRI 2021 Digital webinar
Presentations
- Looking beyond the hype: Applied AI and machine learning in translational medicine, Dennis Wang
- Computational network pharmacology in cardiovascular disease – leveraging big data, Joseph Loscalzo
- Utilizing artificial intelligence to screen and diagnose iPAH, David Kiely
- Biological heterogeneity in idiopathic pulmonary arterial hypertension identified through unsupervised transcriptomic profiling of whole blood, Sokratis Kariotis
- Transcriptomic characterization of Pulmonary arterial hypertension on pathway level, Yajing Ji
Polls
Start of webinar poll
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%