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DATE 2016-10-01

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MESSAGE
DATE 2016-10-27
FROM Christopher League
SUBJECT Re: [Learn] Phylogenetics educational links
From learn-bounces-at-nylxs.com Thu Oct 27 16:09:40 2016
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Subject: Re: [Learn] Phylogenetics educational links
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Here's a set of introductory slides on inference of phylogenetic trees.



Based on what I've learned today, "small" parsimony algorithms like
Fitch and Sankoff rely on labeling a *given* tree shape (aka topology),
so we already have to know (or hypothesize) the ancestral relationships.
The algorithm just determines which labels (mutations) to assign to
interior nodes. That's really unsatisfying to me.

But the "big" parsimony techniques have to search the entire tree space,
which is ENORMOUS. The problem is formally NP-hard. Now -- people do
manage to solve (or approximately solve) NP-hard problems every day by
using piles of dirty tricks. When it comes to searching gigantic spaces,
those dirty tricks are the classic techniques of artificial
intelligence.

So the lecture slides get into techniques like greedy algorithms,
hill-climbing, simulated annealing, genetic algorithms, etc. Anyway,
there could be a lot of meat here that fits under the heading of AI +
phylogenetics. It's much more accessible stuff than trying to
automatically interpret 3D model data to take measurements of maxillary
bones.

CL


Ruben Safir writes:

> http://telliott99.blogspot.com/2010/03/fitch-and-sankoff-algorithms-for.h=
tml
>
> "The Fitch algorithm considers the sites (or characters) one at a time. A=
t each tip in the tree, we create a set containing those nucleotides (state=
s) that are observed or are compatible with the observation. Thus, if we se=
e an A, we create the set {A}. If we see an ambiguity such as R, we create =
the set {AG}. Now we move down the tree [away from the tips]. In algorithmi=
c terms, we do a postorder tree traversal. At each interior node we create =
a set that is the intersection of sets at the two descendant nodes. However=
, if that set is empty, we instead create the set that is the union of the =
two sets at the descendant nodes. Every time we create such a union, we als=
o count one change of state."
>
>
> https://en.wikipedia.org/wiki/Non-parametric
> Nonparametric statistics are statistics not based on parameterized famili=
es of probability distributions. They include both descriptive and inferent=
ial statistics. The typical parameters are the mean, variance, etc. Unlike =
parametric statistics, nonparametric statistics make no assumptions about t=
he probability distributions of the variables being assessed. The differenc=
e between parametric models and non-parametric models is that the former ha=
s a fixed number of parameters, while the latter grows the number of parame=
ters with the amount of training data.[1] Note that the non-parametric mode=
l does, counterintuitively, contain parameters: the distinction is that par=
ameters are determined by the training data in the case of non-parametric s=
tatistics, not the model.
>
> https://en.wikipedia.org/wiki/Fitch-Margoliash_algorithm
> Distance-matrix methods
>
> Distance-matrix methods of phylogenetic analysis explicitly rely on a mea=
sure of "genetic distance" between the sequences being classified, and ther=
efore they require an MSA (multiple sequence alignment) as an input. Distan=
ce is often defined as the fraction of mismatches at aligned positions, wit=
h gaps either ignored or counted as mismatches.[1] Distance methods attempt=
to construct an all-to-all matrix from the sequence query set describing t=
he distance between each sequence pair. From this is constructed a phylogen=
etic tree that places closely related sequences under the same interior nod=
e and whose branch lengths closely reproduce the observed distances between=
sequences. Distance-matrix methods may produce either rooted or unrooted t=
rees, depending on the algorithm used to calculate them. They are frequentl=
y used as the basis for progressive and iterative types of multiple sequenc=
e alignment. The main disadvantage of distance-matrix methods is their inab=
ility to efficiently use=20
> information about local high-variation regions that appear across multip=
le subtrees.[2]
>
>
> --=20
> So many immigrant groups have swept through our town
> that Brooklyn, like Atlantis, reaches mythological
> proportions in the mind of the world - RI Safir 1998
> http://www.mrbrklyn.com=20
>
> DRM is THEFT - We are the STAKEHOLDERS - RI Safir 2002
> http://www.nylxs.com - Leadership Development in Free Software
> http://www2.mrbrklyn.com/resources - Unpublished Archive
> http://www.coinhangout.com - coins!
> http://www.brooklyn-living.com
>
> Being so tracked is for FARM ANIMALS and and extermination camps,
> but incompatible with living as a free human being. -RI Safir 2013
> _______________________________________________
> Learn mailing list
> Learn-at-nylxs.com
> http://lists.mrbrklyn.com/mailman/listinfo/learn

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1.0, user-scalable=3Dyes">




Here=E2=80=99s a set of introductory slides on inference of phylogenetic=
trees.


pdf" class=3D"uri">http://www.cs.otago.ac.nz/cosc348/phylo/Lecture14_PhyloO=
ptim.pdf


Based on what I=E2=80=99ve learned today, =E2=80=9Csmall=E2=80=9D parsim=
ony algorithms like Fitch and Sankoff rely on labeling a given tre=
e shape (aka topology), so we already have to know (or hypothesize) the anc=
estral relationships. The algorithm just determines which labels (mutations=
) to assign to interior nodes. That=E2=80=99s really unsatisfying to me.


But the =E2=80=9Cbig=E2=80=9D parsimony techniques have to search the en=
tire tree space, which is ENORMOUS. The problem is formally NP-hard. Now =
=E2=80=93 people do manage to solve (or approximately solve) NP-hard proble=
ms every day by using piles of dirty tricks. When it comes to searching gig=
antic spaces, those dirty tricks are the classic techniques of artificial i=
ntelligence.


So the lecture slides get into techniques like greedy algorithms, hill-c=
limbing, simulated annealing, genetic algorithms, etc. Anyway, there could =
be a lot of meat here that fits under the heading of AI + phylogenetics. It=
=E2=80=99s much more accessible stuff than trying to automatically interpre=
t 3D model data to take measurements of maxillary bones.


CL


Ruben Safir ruben-at-mrbrklyn.com=
writes:



http://telliott99.blogspot.com/2010/03/fitch-and-sankoff-algorithms-for.=
html


=E2=80=9CThe Fitch algorithm considers the sites (or characters) one at =
a time. At each tip in the tree, we create a set containing those nucleotid=
es (states) that are observed or are compatible with the observation. Thus,=
if we see an A, we create the set {A}. If we see an ambiguity such as R, w=
e create the set {AG}. Now we move down the tree [away from the tips]. In a=
lgorithmic terms, we do a postorder tree traversal. At each interior node w=
e create a set that is the intersection of sets at the two descendant nodes=
. However, if that set is empty, we instead create the set that is the unio=
n of the two sets at the descendant nodes. Every time we create such a unio=
n, we also count one change of state.=E2=80=9D


https://en.wikipedia.org/wiki/Non-parametric Nonparametric statistics ar=
e statistics not based on parameterized families of probability distributio=
ns. They include both descriptive and inferential statistics. The typical p=
arameters are the mean, variance, etc. Unlike parametric statistics, nonpar=
ametric statistics make no assumptions about the probability distributions =
of the variables being assessed. The difference between parametric models a=
nd non-parametric models is that the former has a fixed number of parameter=
s, while the latter grows the number of parameters with the amount of train=
ing data.[1] Note that the non-parametric model does, counterintuitively, c=
ontain parameters: the distinction is that parameters are determined by the=
training data in the case of non-parametric statistics, not the model.


https://en.wikipedia.org/wiki/Fitch-Margoliash_algorithm Distance-matrix=
methods


Distance-matrix methods of phylogenetic analysis explicitly rely on a me=
asure of =E2=80=9Cgenetic distance=E2=80=9D between the sequences being cla=
ssified, and therefore they require an MSA (multiple sequence alignment) as=
an input. Distance is often defined as the fraction of mismatches at align=
ed positions, with gaps either ignored or counted as mismatches.[1] Distanc=
e methods attempt to construct an all-to-all matrix from the sequence query=
set describing the distance between each sequence pair. From this is const=
ructed a phylogenetic tree that places closely related sequences under the =
same interior node and whose branch lengths closely reproduce the observed =
distances between sequences. Distance-matrix methods may produce either roo=
ted or unrooted trees, depending on the algorithm used to calculate them. T=
hey are frequently used as the basis for progressive and iterative types of=
multiple sequence alignment. The main disadvantage of distance-matrix meth=
ods is their inability to efficiently use information about local high-vari=
ation regions that appear across multiple subtrees.[2]


=E2=80=93 So many immigrant groups have swept through our town that Broo=
klyn, like Atlantis, reaches mythological proportions in the mind of the wo=
rld - RI Safir 1998 http://www.mrbrklyn.com


DRM is THEFT - We are the STAKEHOLDERS - RI Safir 2002 http://www.nylxs.=
com - Leadership Development in Free Software http://www2.mrbrklyn.com/reso=
urces - Unpublished Archive http://www.coinhangout.com - coins! http://www.=
brooklyn-living.com


Being so tracked is for FARM ANIMALS and and extermination camps, but in=
compatible with living as a free human being. -RI Safir 2013 ______________=
_________________________________ Learn mailing list Learn-at-nylxs.com http:/=
/lists.mrbrklyn.com/mailman/listinfo/learn






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Learn-at-nylxs.com
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--===============0074659448==--

  1. 2016-10-04 ruben safir <ruben-at-mrbrklyn.com> Re: [Learn] Check List of Texts to learn Cladistics and
  2. 2016-10-04 Ruben Safir <ruben-at-mrbrklyn.com> Re: [Learn] Library access
  3. 2016-10-04 Ruben Safir <ruben-at-mrbrklyn.com> Re: [Learn] Library access
  4. 2016-10-04 Christopher League <christopher.league-at-liu.edu> Re: [Learn] phylogenetics
  5. 2016-10-04 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] 3rd scans and displays for msueums
  6. 2016-10-04 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] =?utf-8?q?Residual_diversity_estimates=E2=80=99_do_not_co?=
  7. 2016-10-04 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] another paper on this topic to dig through
  8. 2016-10-04 Ruben Safir <ruben.safir-at-my.liu.edu> Subject: [Learn] Basic Phylogeny and Systematics
  9. 2016-10-04 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] Library access
  10. 2016-10-05 Ruben Safir <mrbrklyn-at-panix.com> Re: [Learn] 3rd scans and displays for msueums
  11. 2016-10-05 ruben safir <ruben-at-mrbrklyn.com> Subject: [Learn] Fwd: Re: Check List of Texts to learn Cladistics and
  12. 2016-10-06 From: "Ruben.Safir" <ruben.safir-at-my.liu.edu> Subject: [Learn] TNT - Boom
  13. 2016-10-06 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] tomorrow
  14. 2016-10-09 ruben safir <ruben-at-mrbrklyn.com> Subject: [Learn] Fwd: phylogeny tyrannosauroid dinosaurs
  15. 2016-10-09 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] Fwd: Re: phylogeny tyrannosauroid dinosaurs
  16. 2016-10-09 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] Fwd: Re: [dinosaur] phylogeny tyrannosauroid dinosaurs
  17. 2016-10-10 Ruben Safir <ruben-at-mrbrklyn.com> Re: [Learn] Paleo meeting time
  18. 2016-10-10 Ruben Safir <ruben-at-mrbrklyn.com> Re: [Learn] phylogeny tyrannosauroid dinosaurs
  19. 2016-10-10 Steve Brusatte <brusatte-at-gmail.com> Re: [Learn] phylogeny tyrannosauroid dinosaurs
  20. 2016-10-10 Ruben Safir <ruben-at-mrbrklyn.com> Re: [Learn] phylogeny tyrannosauroid dinosaurs
  21. 2016-10-10 From: =?UTF-8?B?RGF2aWQgxIxlcm7DvQ==?= <david.cerny1-at-gmail.com> Re: [Learn] [dinosaur] phylogeny tyrannosauroid dinosaurs
  22. 2016-10-10 Ruben Safir <ruben-at-mrbrklyn.com> Re: [Learn] [dinosaur] phylogeny tyrannosauroid dinosaurs
  23. 2016-10-10 Ruben Safir <ruben-at-mrbrklyn.com> Re: [Learn] [dinosaur] phylogeny tyrannosauroid dinosaurs
  24. 2016-10-10 Dalton Meyer <paleonerd12-at-gmail.com> Re: [Learn] [dinosaur] phylogeny tyrannosauroid dinosaurs
  25. 2016-10-10 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] AI research
  26. 2016-10-10 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] CTScan in paelo
  27. 2016-10-10 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] Fwd: Re: [dinosaur] phylogeny tyrannosauroid dinosaurs
  28. 2016-10-10 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] http://palaeos.com/phylogeny/index.html
  29. 2016-10-10 Ruben Safir <ruben.safir-at-my.liu.edu> Subject: [Learn] maxillary fenestra and promaxillary fenestra
  30. 2016-10-10 Christopher League <league-at-contrapunctus.net> Subject: [Learn] Paleo meeting time
  31. 2016-10-10 Christopher League <league-at-contrapunctus.net> Subject: [Learn] Paleo meeting time
  32. 2016-10-10 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] promaxillary fenestra
  33. 2016-10-13 Ruben Safir <ruben-at-mrbrklyn.com> Re: [Learn] Invitation: Phylogenetics project mtg -at- Thu 2016-10-13
  34. 2016-10-13 Ruben Safir <ruben-at-mrbrklyn.com> Re: [Learn] Library access
  35. 2016-10-13 ruben safir <ruben-at-mrbrklyn.com> Subject: [Learn] Fwd: access to the screen
  36. 2016-10-13 ruben safir <ruben-at-mrbrklyn.com> Subject: [Learn] Fwd: access to the screen
  37. 2016-10-13 ruben safir <ruben-at-mrbrklyn.com> Subject: [Learn] graphics
  38. 2016-10-14 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] a new generation discovers freedom
  39. 2016-10-14 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] Fwd: Check out this picture...
  40. 2016-10-14 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] Fwd: Check out this picture...
  41. 2016-10-14 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] Fwd: Check out this picture...
  42. 2016-10-14 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] Fwd: Check out this picture...
  43. 2016-10-15 Ruben Safir <ruben-at-mrbrklyn.com> Re: [Learn] 3d scanning in the field
  44. 2016-10-15 Ruben Safir <mrbrklyn-at-panix.com> Subject: [Learn] 3d scanning in the field
  45. 2016-10-16 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] Jobs
  46. 2016-10-19 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] tomorrows schedule and plan got the week
  47. 2016-10-20 Christopher League <league-at-contrapunctus.net> Re: [Learn] tomorrows schedule and plan got the week
  48. 2016-10-21 Ruben Safir <ruben-at-mrbrklyn.com> Re: [Learn] tomorrows schedule and plan got the week
  49. 2016-10-21 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] publication
  50. 2016-10-21 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] thesis resources
  51. 2016-10-25 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] Phylogenetics educational links
  52. 2016-10-27 Christopher League <league-at-contrapunctus.net> Re: [Learn] Phylogenetics educational links
  53. 2016-10-27 Ruben Safir <mrbrklyn-at-panix.com> Re: [Learn] Phylogenetics educational links
  54. 2016-10-27 Ruben Safir <mrbrklyn-at-panix.com> Re: [Learn] Phylogenetics educational links
  55. 2016-10-27 Ruben Safir <ruben-at-mrbrklyn.com> Re: [Learn] Phylogenetics educational links
  56. 2016-10-27 Ruben Safir <mrbrklyn-at-panix.com> Re: [Learn] Phylogenetics educational links
  57. 2016-10-30 Ruben Safir <ruben-at-mrbrklyn.com> Subject: [Learn] Orders for the Thesis,
  58. 2016-10-31 Christopher League <league-at-contrapunctus.net> Re: [Learn] cuda kernels
  59. 2016-10-31 Ruben Safir <ruben.safir-at-my.liu.edu> Re: [Learn] cuda kernels
  60. 2016-10-31 Christopher League <league-at-contrapunctus.net> Re: [Learn] cudaMallac
  61. 2016-10-31 Ruben Safir <mrbrklyn-at-panix.com> Re: [Learn] cudaMallac
  62. 2016-10-31 Christopher League <league-at-contrapunctus.net> Subject: [Learn] Computational evolution
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