From listmaster animalgenome.org Fri Aug 2 15:43:05 2019
Return-Path: <listmasteranimalgenome.org>
From: Joanna Szyda <jszydagmail.com>
Subject: Re: Guide on deep learning
Postmaster: submission approved by list moderator
To: Members of AnGenMap <angenmapanimalgenome.org>
Date: Fri, 02 Aug 2019 15:43:05 -0500
Ignacy, why worry?
Nowadays, the Methods section in most journals is located at the end of the
paper, with a small font. If you have many individuals/many omics data/
fancy phenotypes/fancy graphics the methodology does not matter. Even
a single SNP GWAS with no environment precorrection will do. There are many
great R packages to download, so why bother with developing new methods if
we can have more papers published instead?
... I am a very, very, very poor R programmer, but I do not worry. The
"language" keeps impressing me, I never get error messages, its great! My
latest favorite is: v1<-c(1,2,3); v1[50]=11 (no error)
Cheers,
Joanna
BTW Does anyone read whole papers anymore?
_______________________
Joanna Szyda, professor
Institute of Animal Genetics, Biostatistics Lab
Wroclaw University of Life Sciences
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
mail: Kozuchowska 7, PL-51631 Wroclaw, Poland
Tel: +48-(0)71-3205846
email: http://theta.edu.pl/
_____________________________________________________________
On Thu, 1 Aug 2019 at 20:46, Ignacy Misztal <ignacyuga.edu> wrote:
> Very funny but relevant message by Daniel. Is SHALLOW DEEPly out of
> fashion?
>
> Miguel Perez-Enciso visited UGA a couple of weeks ago and gave a short
> course on machine learning. We had many discussions, and although Miguel is
> capable of sophisticated theories, he adapts to new (good or bad) times.
>
> Some 25+ years ago the animal breeding was full of sophisticated theories
> with long derivations. Write a model, derive derivatives, estimate
> parameters... Took a guru to derive anything, but there were many gurus at
> that time. E.g., see formulas for threshold models (Gianola, Fouley,
> Hoeschele,..) or for AI REML (Thompson, Jensen,..). Brrrrrr
>
> Next came models based on sampling (sometimes termed Bayesian although BLUP
> is Bayesian too) strongly advocated by Daniel Gianola. Write a model but
> sample instead of deriving much. What needed pages of formulas could then
> be described in few formulas for sampling. Combinations of
> threshold/linear/censored models are now trivial to implement! Except that
> costs are high, results need interpretation, and the methodology is not
> really suitable for large evaluations. But good for papers and requiring
> less class time for students.
>
> The next step is machine learning. Do not even formulate a model, let
> machine do it from a list of cryptic choices. Would you trust machine
> learning in commercial decisions in animal breeding? Not sure but DEEP is
> good for papers and requires even less class time. Also, DEEP is good when
> you need to have results (of whatever quality) in seconds.
>
> Recently, the US public television (PBS) produced a program "Nova:
> Predictions by the numbers" that cited Fisher as originator of statistical
> significance, also commenting on the Bayes theorem. The program was ranked
> by viewers as 2 out of 5. Complexity seems to be out of fashion nowadays.
>
> Ignacy Misztal
>
>> -----Original Message-----
>> From: DANIEL GIANOLA <gianolaansci.wisc.edu>
>> Sent: Monday, July 22, 2019 12:11 PM
>> To: Members of AnGenMap <angenmapanimalgenome.org>
>> Subject: Re: Guide on deep learning
>>
>> A DEEP THANK YOU, Miguel and Laura. Hopefully, we will all learn DEEPly,
>> and see if knowledge of the DEEP architecture of complex traits can ever
>> be learned.
>>
>> We certainly cannot predict with the SHALLOW y=Xb+e (GWAS), and it is
>> argued that if the whole-genome is implicated, then GWAS is not informative
>> (Tam et al. 2019, Nat. Rev. Genetics). That line of reasoning implies that
>> perhaps we cannot learn much with the less shallow SHALLOW y=Xb+Zu+e--all
>> loci get implicated-- although predict we certainly can!
>>
>> Maybe DEEP learning will implicate the whole-genome, the whole
>> interactome, >> the increasing army of DEEP phenotypes (images, sounds,
>> odor--good and bad ones, whatsapp, etc), and eventually, a DEEP MULTI-OMICS
>> SINGLE STAGE BLUP will become a focal topic in the next five years. Perhaps,
>> Ignacy Misztal will extend APY to DEEPY and Cheng-Fernando-Garrick will
>> develop DEEP Bayes Cpi- pipi-pipipi, etc. Here pi means first layer, pipi
>> means the second layer, and pipipipi stands for higher-dimensional stuff.
>>
>> Another possibility is that we will drown in a DEEP sea of confusion,
>> akin to the situation of choosing a beer in a good Belgian brasserie or
>> extracting a meaningful message from the UN General Assembly.
>>
>>
>> Regards,
>>
>> Daniel
>>
>> ________________________________
>> .From: miguel.perez <miguel.perezuab.es>
>> .Sent: Monday, July 22, 2019 8:02 AM
>> .To: Members of AnGenMap <angenmapanimalgenome.org>
>> .Subject: Guide on deep learning
>>
>> Just in case you are wondering about how deep learning works, we have
>> posted an easy to follow script in github guide
>> https://github.com/...rezenciso/DLpipeline
>> and associated, somewhat more technical reference in Genes, 10, 553.
>>
>> https://www.mdpi.com/2073-4425/10/7/553
>>
>> --
>> ===============================================================
>> Miguel Perez-Enciso
>> ICREA professor
>> Centre for Research in Agricultural Genomics (CRAG) and Facultat de
>> Veterinaria
>> UAB Campus Universitat Autonoma Barcelona Bellaterra
>> E-08193 Spain
>> Tel: +34 935636600 ext 3346
>> Fax: +34 935636601
>> miguel.perezuab.es
>> http://www.icrea.cat/...Enciso-255
>> http://bioinformatics.cragenomica.es/numgenomics/
>> http://scholar.google.es/...r=Lpl_-dcAAAAJ&hl=es
>> http://orcid.org/0000-0003-3524-995X
>> https://github.com/miguelperezenciso/
>> ================================================================
|
