Arnaud Dhaene

MSc Thesis @Stanford | Computational Life Science @EPFL

[Arnaud Dhaene @arnaudhaene]

@json_yim @HannesStaerk Looks cool! Is it as flexible as RFdiffusion in the sense of being able to do motif scaffolding, fold conditioning, and binder design?

[Arnaud Dhaene @arnaudhaene]

@pwang I use Todoist, it integrates well with Fantastical

[Arnaud Dhaene @arnaudhaene]

All you need to do is wrap your original list object in a FuncList object.

https://t.co/KnDeqMUT7J

[Arnaud Dhaene @arnaudhaene]

I always felt that some of the functional tools that come with standard Python were insufficient and awfully verbose.

[Arnaud Dhaene @arnaudhaene]

This is why I decided to create a little wrapper around the emblematic Python list that allows calling {filter, map, reduce, flatmap} on the list element itself. It's definitely a WIP, but my lists are now unencumbered and my code (more) readable!

[Will Geary @wgeary]

Unpopular data science opinion: no body uses dashboards

[Arnaud Dhaene @arnaudhaene]

@marty You can simply sign into an account for a Google Chrome session window and all Google links will follow that account

[jack @jackbutcher]

Give fewer choices, get more action.

[Arnaud Dhaene @arnaudhaene]

@bindureddy Completely agree with you, despite it being a weird setting to use the word “crisply”

[Arnaud Dhaene @arnaudhaene]

https://t.co/05CfOAAqk1

[Arnaud Dhaene @arnaudhaene]

What's the probability that you are infected with COVID-19 given that your RT-PCR test came back positive? 🤔

Hint: it's lower than you think!

Let's find out using Bayes' Theorem👇

[Arnaud Dhaene @arnaudhaene]

For the sake of the calculation, let us assume a 95% true-positive and a 2.3% false-positive rate based on the paper below (UK).

Let us use the current infection rate in the UK: 273 per 100k residents (7-day rolling average) means P(C) = 0.00273 👇

https://t.co/uvxk1AhlP0

[Arnaud Dhaene @arnaudhaene]

People are not getting tested randomly! Most people who get swabbed have a reason, they might display symptoms, recent contact, etc. This increases the probability of getting tested, AND of being infected!

Lesson learned: never forget confounders! 🕵️‍♂️

[Arnaud Dhaene @arnaudhaene]

P(C | T) = 0.95 / (0.95 * 0.00273 + 0.023 * 0.99727) * 0.00273
P(C | T) = 0.1015 or 10.15%

Putting all of this together, we find that the probability you are infected given a positive test result is only 10.15%!

Wait, is this true? How can this be? 👇

[Arnaud Dhaene @arnaudhaene]

@chrisalbon Looking forward to this!

[Arnaud Dhaene @arnaudhaene]

@kareem_carr I think an (the most) important part that’s left out is the causality models humans use to make sense of the data, link them to domain expertise and define the ML tasks and relevant statistics.

[Arnaud Dhaene @arnaudhaene]

@Vuhlandes y so butthurt