👋 @LucyStats here! It's been a very exciting week for folks in Causal Inference with the Nobel Prize announcements, I thought it'd be neat to dive back in history to hear about a previous Nobel winner, Ronald Ross
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This topic is fun because it spans a whole myriad of my interests!
✔️We've got stats!
✔️We've got poetry!
✔️We've got infectious disease epidemiology!
Ronald Ross won the Nobel Prize for Physiology or Medicine in 1902 "for his work on malaria, by which he has shown how it enters the organism and thereby has laid the foundation for successful research on this disease and methods of combating it."
This was based on a discovery about malaria and mosquitoes. Somewhat famously, after making this discovery this polymath wrote a poem!
I love hearing about quantitative folks living out loud with their humanities talents!
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Then Ross turned to trying to predict the magnitude of malaria outbreaks. He came up with a mechanistic model that predicted the number of new infections per month based current epi parameters 👇
This led Ross to come up with the "critical mosquito density" to show when the malaria epidemic would die out. Previously, folks thought that only *complete* elimination of mosquitos would stop the spread.
Turns out his theory seemed to work! If they could get the mosquito density down to fewer than 40 mosquitoes per person in the population, malaria cases decreased
Something I ❤️ about this example is the combination of mechanistic modeling and statistics! Ross hypothesized a mechanistic relationship (via the original equations) and used these to inform "policy". He then used statistics to collect data & confirm whether it worked
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This led to using differential equations to represent disease dynamics.
Does this sound familiar?! The concept of the basic reproductive number is equivalent to Ross’s "critical mosquito density"!
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If you'd like to know more about mechanistic models (and how to quantify uncertainty in them!), check out this paper @khgrantz, @EpiEllie, & I wrote:
So there you have it! A bit of history about a previous Nobel winner. You could maybe even consider him a *biostatistician* 🏆, his biographers did (see next tweet!)
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"If Ross had been born 100 years later he could have become an eminent biostatistician. His thinking in this area was well ahead of his time and perhaps did not achieve the recognition it deserved until much later." -Nye and Gibson (1997, p. 279)
[pubmed.ncbi.nlm.nih.gov/12762435/]
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You know how excited @daniela_witten gets about SVD? I have about the same thing with kernels. Except that I'm not sure I explain them as well as she does SVD. Still, you're getting a thread on kernels!
Maybe one way of putting it is that kernels are dot products on steroid. The dot product is already pretty cool.
1) It's easy to compute and you learn about it in high school math (at least I did, who knows what kids learn in high school now).
Take two p-dimensional vectors x = (x1, x2, ..., xp) and y = (y1, y2, ..., yp), their dot product <x|y> is simply the sum of the product of their coordinates:
<x|y> = x1 y1 + x2 y2 + ... + xp yp.
Good morning everybody! Let's talk a bit about how I came to develop statistical / machine learning tools for genomics, healthcare and drug discovery.
I trained as an engineer at @IMTAtlantique, with a specialization in computer science. I didn't really enjoy statistics and graduated in 2005, back when AI belonged to scifi and nobody knew what machine learning was.
@IMTAtlantique What really interested me was bioinformatics - the idea that my training in maths and computer science could be put to use to help solve problems from the life sciences was very appealing! So I jumped at the opportunity to intern in a lab that was doing just that.
OK, so a bit of background about me: I'm French (and tweeting from Paris), and I'm currently an associate professor at an engineering school called @MINES_ParisTech.
@MINES_ParisTech The research group I'm in (CBIO) has a partnership with @institut_curie, which is a cancer research institute. CBIO has four PIs, working on various topics related to, you've guessed it, statistics / machine learning & cancer.
@MINES_ParisTech@institut_curie My plan for the week is to talk more about my career path, my research topics, and my love of kernels. Of course I'll also talk about what we do at @WiMLDS_Paris, about open/reproducible science, and about teaching machine learning!
I have organized multiple conferences over the years.
Tips to conference organizers to support women at your meeting
1- Actively consider gender and career stage balance in speakers. 2- Women and minorities may take a longer route to success, try to avoid ageist selection.
3- Provide lactation rooms (with equipment & milk storage). Pumps are heavy and a pain to carry around a meeting. The room should be close-by not a long walk away
4.- Small babies are welcome. Check there is a changing table accessible to dads & mums.
5.- Parents of young children are often postdocs, junior faculty who need and are grateful for childcare and/or travel scholarships.
6- Go Hybrid. Live stream & record talks. Its great if one is stuck in a lactation room, or watching remotely
@Bioconductor provides genome annotation for thousands of species and its packages are used in almost every biological discipline including
Immunology
Oncology
Evolution and Phylogenetics
cheminformatics
comparative genomics
epigenetics
pharamacogenomics
systems biology
etc
The core team with the community create standard class structures for data. Developers create methods that use these, creating a connected framework were packages work together and provide entire analysis workflows
The current release @Bioconductor 3.14, consists of 2083 #RStats packages, 408 experiment data packages, 904 annotation packages, 29 workflows and 8 books.