Re: [Obi-devel] New transformation terms from MO

[William B. <Wil...@Dr...>]

Most definitely, yes.

These are types of quantitative_value which is already in OBI (though  
BFO debates may lead to changing where it ultimately resides in the  
OBI graph).

Having said this, you will note the current PATO ontology contains a  
myriad of value types - quantitative, qualitative, relative, absolute  
- and subtypes for specific types of values such as speed, color  
intensity, concentration, etc.  These are clustered into a "GO SLIM"  
- the OBO format way of creating accessible subsets within an ontology.

It may be we'll ultimately want to see OBI values reside in PATO - at  
least, that would be the emerging OBO Foundry way.  :-)

There do also appear to be "unit" types in PATO, so this is another  
issue we'd need to sort through re: the developing Units Ontology.

Chris Mungall will be present at the OBI meeting, so he can  
definitely help us all to better understand how PATOs values are  
intended to be used by the community.

Cheers,
Bill



On Jan 26, 2007, at 3:22 PM, Tina Hernandez-Boussard wrote:

> Hi Ryan and Philippe,
>
> Here are some additional data transformation terms from MO.  I  
> think that these terms should not go under data transformation, but  
> would be better placed under mathematical terms, as they do not  
> transform the data.
>
> Can you please let me know if you are in agreement so that I can  
> get the final list of data transformation term out before Monday.
>
> Thanks,
>
> Tina
>
>
> NAME:cosine_distance	DEF:The cosine distance of two vectors is the  
> cosine of the angle between them. This measures the difference in  
> direction between two vectors, irrespective of their lengths.	MO
> NAME:Euclidean_distance	DEF:The straight line distance between two  
> points. In n dimensions, the Euclidean distance between two points  
> p and q is square root of (sum (pi-qi)2) where pi (or qi) is the i- 
> th coordinate of p (or q).	MO
> NAME:manhattan_distance	DEF:The "Manhattan distance" is the  
> shortest path between two points in a block format, e.g. the length  
> of the path along Manthattan city streets.	MO
> NAME:pearson_correlation_coefficient	DEF:The Pearson's correlation  
> coefficient between two variables. Its values can range between  
> -1.00 to +1.00. The closer the absolute value of the Pearson  
> correlation coefficient is to 0, the smaller the linear  
> relationship between the two variables. A Pearson correlation  
> coefficient with absolute value 1 indicates perfect linear  
> relationship.	MO
> NAME:Spearmans_rank_correlation	DEF:Computed as the ordinary  
> Pearson correlation coefficient between two groups of rankings.	MO
> NAME:tau_rank_correlation	DEF:a nonparametric measure of the  
> agreement between two rankings	MO
> NAME:french_railway_distance	DEF:The "French railway distance" is  
> based on the fact that (at least in the past) most of the railways  
> in France headed straight to Paris. Thus, the French railway  
> distance between two points is the usual distance if the straight  
> line through them passes to a designated ?Paris? point, or is the  
> sum of their distances to the ?Paris? point otherwise.	MO
> NAME:jackknife_Pearson_correlation	DEF:The jackknife Pearson  
> correlation is the lowest Pearson correlation between two data  
> series where one pair of values in the data series are omitted.	MO
> NAME:uncentered_Pearson_correlation	DEF:The uncentered Pearson  
> correlation is defined as the Pearson correlation for two data  
> series where the mean of each data series is assumed to be zero.	MO
> NAME:Pearson_correlation	DEF:The Pearson correlation is defined as  
> the covariance of two data series divided by the product of their  
> standard deviations.	MO
>
>
>
> Cheers,
>
> Tina B
>
>
>
>
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Bill Bug
Senior Research Analyst/Ontological Engineer

Laboratory for Bioimaging  & Anatomical Informatics
www.neuroterrain.org
Department of Neurobiology & Anatomy
Drexel University College of Medicine
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Philadelphia, PA    19129
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Please Note: I now have a new email - Wil...@Dr...