Re: [ontolog-forum] Genetic discovery using ontology mapping ofobservati

["Rich Cooper" <rich@xxxxxxxxxxxxxxxxxxxxxx>]

Dear John and Michael,

RC

> Could a genetic ontology be useful for mapping the disease biochemistry

> and environmental exposures to genetic profiles?  Perhaps such an ontology

> could be constructed automatically, step by step, through identifying

> subjects with known genetic spectrum and known environmental exposures

> versus diagnosed conditions.

By that description, I am envisioning a system that searches (think And/Or graph search) for the best explanation in a logical combination of evidence fragments.  There are probably lots of diseases that require multiple facts (genes, environmental facts, diagnoses).  There certainly are lots of XML based EHR data files now, and with the 2013 deadline on EHR usage set by Bush already here, there will be millions of them in the near future. 

So a discovery system that browses through all that data could put together logical combinations of evidence from each case, and produce millions of explanations as logical combinations of evidence terminals.  Its that "logical combinations of evidence terminals" that I am calling (perhaps inelegantly) an ontology. 

The reason I call it an ontology, even though it was not put together by humans, is because it represents the tightest explanation of the evidence in Occam's sense.  Whether the explanations are comprehensible in medical terms is another story - one I am willing to ignore for generating the ontology. 

MB

> But why should ontology-based tools be better than the tools already used?

> You would have to have a close look at those tools and the problems they

> solve to answer that question.

The reason I think such an automated generator of ontologies might be useful is precisely because there is so much evidence available, that humans can't possibly keep up with it all.  Therefore the human generated ontology is less likely to happen than the automated one. 

JS

>Yes, that is the fundamental question.  Scientists in >every field have

>developed highly sophisticated tools for analyzing and >reasoning about

>their subjects.

>

>In almost all cases, their tools are far more precise, >detailed,

>and sophisticated than the tools currently available for >ontology.

>The Cyc tools are rather sophisticated, but they offer >little help

>for the sciences.  And compared to Cyc, OWL is >pathetic.

Yes but.  All these tools have been built centered around human comprehensibility of the ontology so generated.  It is intended to record and automate human logic, not the logic of evidence at hand that drives the design of these tools.  I am suggesting that perhaps we should jettison the human generation of the ontology and substitute a method for automatic generation of the ontology without concern for whether a human understands the reasoning at any intuitive level.  Instead, the human would simply follow the evidence trail generated by the auto-ontology, and then pursue attempts to implement the solutions generated rather than to try to understand them. 

Once an auto-ontology generated solution is seen to work in experimental confirmation, then the human analysts can begin trying to understand why the logical combination of terminal evidence acts as it does to produce the disease state or to eliminate it.  So the human learning of why it works comes AFTER the ontology has been generated, not before. 

In the first decades of the 1900s, Somebody Yates invented the Yates transform.  It worked so well that later people invented signal processing algorithms based on the Yates transform.  That is the sort of thing I am suggesting here.  But with ontologies, not with signals. 

-Rich

Sincerely,

Rich Cooper

EnglishLogicKernel.com

Rich AT EnglishLogicKernel DOT com

9 4 9 \ 5 2 5 - 5 7 1 2

-----Original Message-----
From: ontolog-forum-bounces@xxxxxxxxxxxxxxxx [mailto:ontolog-forum-bounces@xxxxxxxxxxxxxxxx] On Behalf Of John F Sowa
Sent: Saturday, May 11, 2013 4:57 AM
To: ontolog-forum@xxxxxxxxxxxxxxxx
Subject: Re: [ontolog-forum] Genetic discovery using ontology mapping ofobservations

Rich and Michael,

I agree with Michael's answer to Rich's question, but I'd like to add

a few more comments.

RC

> Could a genetic ontology be useful for mapping the disease biochemistry

> and environmental exposures to genetic profiles?  Perhaps such an ontology

> could be constructed automatically, step by step, through identifying

> subjects with known genetic spectrum and known environmental exposures

> versus diagnosed conditions.

MB

> But why should ontology-based tools be better than the tools already used?

> You would have to have a close look at those tools and the problems they

> solve to answer that question.

Yes, that is the fundamental question.  Scientists in every field have

developed highly sophisticated tools for analyzing and reasoning about

their subjects.

In almost all cases, their tools are far more precise, detailed,

and sophisticated than the tools currently available for ontology.

The Cyc tools are rather sophisticated, but they offer little help

for the sciences.  And compared to Cyc, OWL is pathetic.

MB

> A data model is not an ontology because it serves the needs of a specific

> application.  But what needs does an ontology serve?  This data model /

> ontology distinction puzzles me more and more.

I don't blame you for being puzzled. The simplest explanation is that

they came from different sources.  The term 'data model' originated

in the "database wars" of the 1970s.  There were three competing

technologies, each with its preferred "data model":  hierarchical

(IMS), network (Codasyl DBTG), and relational.

The goal of the conceptual schema was to allow software to access

data in any format, independent of the way it was stored.  In fact,

its goals were similar to the original goals by Tim Berners-Lee

for the Semantic Web.

But the same mentality that dragged the SW down to little more than

YADM (Yet Another Data Model), the hopes for the conceptual schema

were never realized.

John

 

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