Events Calendar

11 Feb
ISP AI Forum: Graphical causal models for integrative analysis of biomedical and clinical data
Event Type

Forums

Topic

Research, Technology

Target Audience

Undergraduate Students, Staff, Faculty, Graduate Students

Tags

ai

Website

https://pitt.co1.qualtrics.com/jfe/fo...

University Unit
Intelligent Systems Program
Hashtag

#isp

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ISP AI Forum: Graphical causal models for integrative analysis of biomedical and clinical data

This is a past event.

Abstract: The advancement of technologies for high-throughput collection of personal data, including environmental, lifestyle, clinical and biomedical data, has inadvertently transformed biology and medicine. Integrating and co-analyzing these different data streams has become the research bottleneck and, in all likelihood, will be a central research topic for the next decade. Machine Learning has shown promise addressing biomedical and clinical problems, especially regarding classification. Causal graphical models allow for inferring potential cause-effect relations on the whole dataset and are by nature interpretable. My group has worked in extending causal learning framework to mixed data types, incorporating prior information and learning causal graphs with latent confounders. In this talk, I will present an overview of causal inference and some of our results on applications on important biomedical and clinical questions in cancer diagnosis and therapy.

Speaker: Dr Takis Benos

RSVP for the Zoom meeting informationhttps://pitt.co1.qualtrics.com/jfe/form/SV_7PM9H7QUWOg6wLA

Friday, February 11 at 12:30 p.m. to 1:30 p.m.

Virtual Event

ISP AI Forum: Graphical causal models for integrative analysis of biomedical and clinical data

Abstract: The advancement of technologies for high-throughput collection of personal data, including environmental, lifestyle, clinical and biomedical data, has inadvertently transformed biology and medicine. Integrating and co-analyzing these different data streams has become the research bottleneck and, in all likelihood, will be a central research topic for the next decade. Machine Learning has shown promise addressing biomedical and clinical problems, especially regarding classification. Causal graphical models allow for inferring potential cause-effect relations on the whole dataset and are by nature interpretable. My group has worked in extending causal learning framework to mixed data types, incorporating prior information and learning causal graphs with latent confounders. In this talk, I will present an overview of causal inference and some of our results on applications on important biomedical and clinical questions in cancer diagnosis and therapy.

Speaker: Dr Takis Benos

RSVP for the Zoom meeting informationhttps://pitt.co1.qualtrics.com/jfe/form/SV_7PM9H7QUWOg6wLA

Friday, February 11 at 12:30 p.m. to 1:30 p.m.

Virtual Event

Event Type

Forums

Tags

ai

University Unit
Intelligent Systems Program
Hashtag

#isp

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