Estimating the Causal Network of Developmental Neurotoxicants Using PyMC3 by Nicoleta Spînu

Talk Abstract

There is a vital need for alternative methods to animal testing to assess compounds for their potency of inducing developmental neurotoxicity such as learning disabilities in children. However, data are often limited and complex in structure. Therefore, Bayesian approaches are perfect to unravel their meaning and create predictive models. In this talk, I will showcase a multilevel probabilistic model and outline how to deal with unbalanced, correlated and missing values. This presentation will be of interest for those willing to learn multilevel modelling in PyMC3, how to deal with missing values for both predictors and outcomes of data matrices, and their application to a real problem in toxicology.

Nicoleta Spînu Twitter @nicospinu

Talk

Nicoleta Spînu

Nicoleta Spînu is a PhD candidate in Computational Toxicology with a background in pharmaceutical sciences and regulatory affairs looking to have her own impact on the protection of human health while promoting animal welfare (Replacement, Reduction and Refinement of animal testing; “the 3Rs”). Research interests include the science of network and causal inference, computational modelling of chemical toxicity, and regulatory toxicology and policy making.


This is a PyMCon 2020 talk

Learn more about PyMCon!

PyMCon is an asynchronous-first virtual conference for the Bayesian community.

We have posted all the talks here in Discourse on October 24th, one week before the live PyMCon session for everyone to see and discuss at their own pace.

If you are available on October 31st you can register for the live session here!, but if you are not don’t worry, all the talks are already available here on Discourse (keynotes will be posted after the conference) and you can network here on Discourse and on our Zulip.

We value the participation of each member of the PyMC community and want all attendees to have an enjoyable and fulfilling experience. Accordingly, all attendees are expected to show respect and courtesy to other attendees throughout the conference and at all conference events. Everyone taking part in PyMCon activities must abide by the PyMCon Code of Conduct. You can report any incident through this from.

If you want to support PyMCon and the PyMC community but you can’t attend the live session, consider donating to PyMC

Do you have suggestions to improve PyMCon? We have an anonymous suggestion box waiting for you

Have you enjoyed PyMCon? Please fill our PyMCon attendee survey. It is open to both async PyMCon attendees and people taking part in the live session.

2 Likes

Hi All,
I found after re-watching the recording that the most important slide might not be as explanatory as I wished. And therefore, I wrote it a little bit differently, and I’ll post it below. I’ll be happy to discuss it during the Q&A session on the 31st of October as well.
Also, I apologise for any abbreviations used and any English mistakes during the talk; I encourage watching it with the subscripts.
Looking forward to chatting with you :blush:

4 Likes

I will take the opportunity to thank Junpeng Lao for his patience and valuable guidance as well as for encouraging me to present the results at the PyMCon. It meant a lot your time and support!
And many thanks to the Organising Committee, you are setting an example on how an online conference should be run :clap:

5 Likes

Happy to help - it is a great talk and looking forward to the paper :slight_smile:
I particularly like how we build a causal model while dealing with multiple mixing value, and PyMC3 really makes it easy with the imputation.
There is also a Multivariate version of the imputation model, looking forward for @nico to share that when ready.

2 Likes