Re: Book-able view of ID as speculative science

On 2005-12-27, topmind <topmind@xxxxxxxxxxxxxxxx> wrote:
> Lilith (Deanne Taylor) wrote:
>> There are two good books on recognizing and classifying patterns --
>> there are others, but I think two good ones are a place to start; one
>> is called "Pattern Recognition" by Theodoridis and Koutroumbas,
>> published by Academic Press. The other is "Pattern Classification" by
>> Duda, Hart, and Stork published by Wiley Interscience. I'll call the
>> former PR, the latter PC.
>>
>> PR's chapters include classifiers based on Bayes decision theory,
>> linear classifiers, nonlinear classfiiers, feature selection (a hot
>> topic), feature generation, several chapters on clustering. PC goes
>> into the same kinds of topics but does go a bit into genetic
>> programming, machine learning, and a few exotic methods. If you're
>> interested in how pattern classification or pattern recognition is
>> done, there are plenty of these kinds of works out there in the
>> engineering and image processing disciplines.
>>
>> That said, there are many different methods used to do searching for
>> patterns. In biology, "function" (however you wish to define it here)
>> is important in context. Many methods applied to the genome are
>> supervised methods, which search for features with some kind of
>> knowledge of the thing they are looking for. This knowlege can be scant
>> or based on a calculated probability based on how well a sequence
>> matches a known profile. There are also unsupervised models, that
>> look for patterns in data without having a priori knowledge of the
>> thing they are looking for, though there is knowledge inherent in the
>> parameters of the model. You basically either have to know what you're
>> looking for (albeit loosely) in supervised models, or have a strict
>> method of searching for something and have a good definition of what
>> constitutes a "pattern" so you can evaluate your results (unsupervised
>> method).
>
>
> This seems to imply that they are looking for something related to
> biology, not just "patterns" per se. They have in mind up front what
> they are looking for.

Do you _always_ argue from a position of complete ignorance?

Did you even look at the two books that Lilith recommended?

Here are links to the amazon listings for the two books:

http://www.amazon.com/gp/product/0123695317/104-6239367-4945542
http://www.amazon.com/gp/product/0471056693/104-6239367-4945542

Do you think these are biology texts? What field do you think the research
that is detailed within comes from?

>> The biggest sandtrap is when you throw any random sequence
>> into a bunch of unsupervised methods. You'll always get something out
>> that looks interesting, but interpreting it so that it means something
>> is another matter.
>>
>> That said, I'm surprised "ID" people haven't gone into the human
>> genome and thrown every pattern matching algorithm they could against
>> it, try to find some random signature that is as likely as any other,
>> but be obscure about it and insist it indicates special design for some
>> theological reason unconnected to the actual genomics. It might sound
>> like fruitcake on a plate, but it can't be any worse than the whole
>> irreducible complexity argument.
>>
>> As far as supervised methods go, there are many successful ones. A
>> baysian method for gene prediction, for instance, are programs like
>> GenScan, that use pre-existing knowledge of gene structure to predict
>> whether or not a span of DNA is likely to contain a certain feature of
>> a gene. There are programs like RepeatMasker, which give the likelihood
>> that a gene sequence contains a signature of a retroelement ( like
>> viral sequences). There are many other algorithms that do pattern
>> searching/matching on known charateristic signatures. There are
>> several other supervised methods that try to find characteristics based
>> on structures or features that are not well-define sequence features,
>> like helix searching, protein motif searching etc.
>>
>> See for example papers in the list here:
>>
>> http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Display&dopt=pubmed_pubmed&from_uid=15852508
>>
>>
>> In unsupervised methods, the model assumes no knowledge a priori and
>> goes out "mining" for interesting results. Those are most difficult
>> because the information isn't very valuable without biological context.
>> There are some papers that are successful in showing some of these
>> methods, here are some:
>>
>> http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Display&dopt=pubmed_pubmed&from_uid=14571370
>>
>
> Look at the abstract from #3 here:
>
> "Novel tools are needed for comprehensive comparisons of interspecies
> characteristics of massive amounts of genomic sequences currently
> available. An unsupervised neural network algorithm, Self-Organizing
> Map (SOM), is an effective tool for clustering and visualizing
> high-dimensional complex data on a single map. We modified the
> conventional SOM, on the basis of batch-learning SOM, for genome
> informatics making the learning process and resulting map independent
> of the order of data input. We generated the SOMs for tri- and
> tetranucleotide frequencies in 10- and 100-kb sequence fragments from
> 38 eukaryotes for which almost complete genome sequences are available.
> SOM recognized species-specific characteristics (key combinations of
> oligonucleotide frequencies) in the genomic sequences, permitting
> species-specific classification of the sequences without any
> information regarding the species. We also generated the SOM for
> tetranucleotide frequencies in 1-kb sequence fragments from the human
> genome and found sequences for four functional categories (5' and 3'
> UTRs, CDSs and introns) were classified primarily according to the
> categories. Because the classification and visualization power is very
> high, SOM is an efficient and powerful tool for extracting a wide range
> of genome information."
>
> (end quote)
>
> They are basically matching similarities and graphing the similarity as
> a presentation. Something like this won't find say an image of Mona
> Lisa or a formula for geometric buildings hidden in there.

You are nothing if not consistent. Consistently ignorant, but consistent
nonetheless.

> While it may identify some "patterns" per se, they are mostly tuned for
> biological research purposes, not finding intelligent encoding.

It's a pity that ignorance doesn't hurt.

> A good many of them seem devoted to pattern matching itself, not really
> the nature of the patterns. For example, it may find 8 occurences of a
> given pattern, but says nothing about that pattern itself (other than
> maybe matching a library of patterns). If the 8-repeat was an image of
> Mona Lisa, nobody would probably catch that because they are not
> looking for such.

Perhaps you should outline how we should look for the Mona Lisa in the
genome. You keep bringing it up, you obviously think it is important.

> They are mostly looking for similarites within
> sequences, among similar species, different species, etc.
>
> They are essentially cross-reference engines. While such may have use
> in intelligent pattern searching, it is a fairly narrow technique and
> should not be considered the only or best approach.

Not that you could actually name another.

Mark
>
>
>>
>> Enjoy --
>> Deanne
>
> -T-
>

.

Relevant Pages

  • Re: Book-able view of ID as speculative science
    ... > is called "Pattern Recognition" by Theodoridis and Koutroumbas, ... > method of searching for something and have a good definition of what ... characteristics of massive amounts of genomic sequences currently ... Map (SOM), is an effective tool for clustering and visualizing ...
    (talk.origins)
  • Re: Part 1 (of 3): What are major aspects of evolutionary theory?
    ... >> one complete genome per phylum, plus extras for the really interesting ... Venter's shotgun sequencing of whole ... with a highly conserved segment between two modertely evolved ... together into two different sequences which share the central part: ...
    (talk.origins)
  • Re: Humans, chimps, wheat and frogs
    ... Depends on what part of the genome you're looking at. ... conserved) sequences are a tiny minority of the genome, ... These stretches of DNA ... As the species specific miRNAs described here are ...
    (talk.origins)
  • Re: Part 1 (of 3): What are major aspects of evolutionary theory?
    ... polymeric genome and evolution without interference from the others. ... Finally several of the polymeric genome species expanded their habitat ... A> hundred thousands guess just for *known* sequences among the very few ... tgtatgagagct appears twice in each, so how do we know which copy is ...
    (talk.origins)
  • Re: The function of non-coding DNA (AKA junk)
    ... transcription from non-protein-coding genomic sequences. ... close to 93% of the genome is transcribed into RNA" ... ...
    (talk.origins)