
Modelling the lanuage of the immune system with machine learning (first steps) - jostmey
https://github.com/jostmey/MaxSnippetModel#statistical-classifiers-for-diagnosing-disease-from-immune-repertoires
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kensai
The paper of the approach:
[https://bmcbioinformatics.biomedcentral.com/articles/10.1186...](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-017-1814-6)

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unwind
Meta: typo in the title, mods please s/lanuage/language/. Thanks.

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joe_the_user
I don't know that original title was but a quick look doesn't show the word
"language" used in the article. "The language of the immune system" really
seems like editorializing.

In fact, the title as I currently see it: "Modelling the lanuage of the immune
system with machine learning (first steps)" seems like a pretty strong title
abuse from start to finish. The title of repository is: "Statistical
classifiers for diagnosing disease from immune repertoires".

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angel_j
A basic classifier without the data, thanks for nothing.

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Gatsky
This is obviously a rubbish comment, but nevertheless it raises an interesting
point. Deep learning has entered a phase where, due to the excellent tooling
(PyTorch, TensorFlow etc) and freely available data, there is a massive
ingress of new poeple into the field. This has had a variety of effects, many
of which are not necessarily beneficial to progress. There is so much noise
and hype that it is difficult to do good work in the first place, and then be
recognised for it. Unsurprisingly, rumours about massive salaries at FAANG,
everyone trying to start AI companies, and a torrent of dubious quality
research flooding conferences retard real progress.

I suspect that there is some optimal mix of making a field accessible and
attracting good people to it. If it is too inaccessible, then progress is too
slow. It it is too accessible, then it gets flooded. Most scientific fields
exist on the inaccessible spectrum. It is relevant to note that many amazing
discoveries (including the foundations of deep learning for example) were made
when the field was obscure.

So to answer your comment, a scientific field doesn't owe you anything, and in
fact, is probably better served by not making it too easy for you to get
involved.

