| 
| 1. Title of Database: Primate splice-junction gene sequences (DNA)
|                       with associated imperfect domain theory
| 
| 2. Sources:
|    (a) Creators: 
|        - all examples taken from Genbank 64.1 (ftp site: genbank.bio.net)
|        - categories "ei" and "ie" include every "split-gene" 
|          for primates in Genbank 64.1
|        - non-splice examples taken from sequences known not to include
|          a splicing site 
|    (b) Donor: G. Towell, M. Noordewier, and J. Shavlik, 
|               {towell,shavlik}@cs.wisc.edu, noordewi@cs.rutgers.edu
|    (c) Date received: 1/1/92
| 
| 3. Past Usage:
|    (a) machine learning:
|        	-- M. O. Noordewier and G. G. Towell and J. W. Shavlik, 1991; 
|            "Training Knowledge-Based Neural Networks to Recognize Genes in 
|            DNA Sequences".  Advances in Neural Information Processing Systems,
|            volume 3, Morgan Kaufmann.
| 
| 	-- G. G. Towell and J. W. Shavlik and M. W. Craven, 1991;  
|            "Constructive Induction in Knowledge-Based Neural Networks",  
|            In Proceedings of the Eighth International Machine Learning
| 	   Workshop, Morgan Kaufmann.
| 
|         -- G. G. Towell, 1991;
|            "Symbolic Knowledge and Neural Networks: Insertion, Refinement, and
|            Extraction", PhD Thesis, University of Wisconsin - Madison.
| 
|         -- G. G. Towell and J. W. Shavlik, 1992;
|            "Interpretation of Artificial Neural Networks: Mapping 
|            Knowledge-based Neural Networks into Rules", In Advances in Neural
|            Information Processing Systems, volume 4, Morgan Kaufmann.
| 
|    (b) attributes predicted: given a position in the middle of a window
|        60 DNA sequence elements (called "nucleotides" or "base-pairs"),
|        decide if this is a
| 	a) "intron -> exon" boundary (ie) [These are sometimes called "donors"]
| 	b) "exon -> intron" boundary (ei) [These are sometimes called "acceptors"]
| 	c) neither                      (n)
|    (c) Results of study indicated that machine learning techniques (neural
|        networks, nearest neighbor, contributors' KBANN system) performed as
|        well/better than classification based on canonical pattern matching
|        (method used in biological literature).
| 
| 4. Relevant Information Paragraph:
|    Problem Description: 
|       Splice junctions are points on a DNA sequence at which `superfluous' DNA is
|       removed during the process of protein creation in higher organisms.  The
|       problem posed in this dataset is to recognize, given a sequence of DNA, the
|       boundaries between exons (the parts of the DNA sequence retained after
|       splicing) and introns (the parts of the DNA sequence that are spliced
|       out). This problem consists of two subtasks: recognizing exon/intron
|       boundaries (referred to as EI sites), and recognizing intron/exon boundaries
|       (IE sites). (In the biological community, IE borders are referred to
|       a ``acceptors'' while EI borders are referred to as ``donors''.)
| 
|    This dataset has been developed to help evaluate a "hybrid" learning
|    algorithm (KBANN) that uses examples to inductively refine preexisting
|    knowledge.  Using a "ten-fold cross-validation" methodology on 1000
|    examples randomly selected from the complete set of 3190, the following 
|    error rates were produced by various ML algorithms (all experiments
|    run at the Univ of Wisconsin, sometimes with local implementations
|    of published algorithms). 
| 
|                 System	       Neither    EI      IE
|                 ----------     -------  -----   -----
| 		KBANN    	 4.62    7.56    8.47
| 		BACKPROP    	 5.29    5.74   10.75
| 		PEBLS    	 6.86    8.18    7.55
| 		PERCEPTRON    	 3.99   16.32   17.41
| 		ID3    		 8.84   10.58   13.99
| 		COBWEB   	11.80   15.04    9.46
| 		Near. Neighbor	31.11   11.65    9.09
| 	     	
|    Type of domain: non-numeric, nominal (one of A, G, T, C)
|  
| 5. Number of Instances: 3190
| 
| 6. Number of Attributes: 62
|    -- class (one of n, ei, ie)
|    -- instance name
|    -- 60 sequential DNA nucleotide positions
| 
| 7. Attribute information:
|    -- Statistics for numeric domains: No numeric features used.
|    -- Statistics for non-numeric domains
|       -- Frequencies:  Neither	    EI	      IE
|                        -------	  ------     -----
| 		A  	24.984%	  22.153%   20.577%
| 		G  	25.653%	  31.415%   22.383%
| 		T  	24.273%	  21.771%   26.445%
| 		C  	25.077%	  24.561%   30.588%
| 		D	 0.001%    --        0.002%
| 		N	 0.010%    0.010%    --
|                 S        --        --        0.002%
|                 R        --        --        0.002% 
| 
|    Attribute #:  Description:
|    ============  ============
|              1   One of {n ei ie}, indicating the class.
|              2   The instance name.
|           3-62   The remaining 60 fields are the sequence, starting at 
|                  position -30 and ending at position +30. Each of
|                  these fields is almost always filled by one of 
|                  {a, g, t, c}. Other characters indicate ambiguity among
|                  the standard characters according to the following table:
| 			character	    meaning
| 			---------	----------------
| 			    D		  A or G or T
| 			    N           A or G or C or T
| 			    S                C or G
| 			    R		     A or G
| 
| 8. Missing Attribute Values: none
| 
| 9. Class Distribution: 
| 	EI:       767  (25%)
|         IE:       768  (25%)
| 	Neither: 1655  (50%)
| 

N, EI, IE

A0: A, T, G, C, N, D, S, R
A1: A, T, G, C, N, D, S, R
A2: A, T, G, C, N, D, S, R
A3: A, T, G, C, N, D, S, R
A4: A, T, G, C, N, D, S, R
A5: A, T, G, C, N, D, S, R
A6: A, T, G, C, N, D, S, R
A7: A, T, G, C, N, D, S, R
A8: A, T, G, C, N, D, S, R
A9: A, T, G, C, N, D, S, R
A10: A, T, G, C, N, D, S, R
A11: A, T, G, C, N, D, S, R
A12: A, T, G, C, N, D, S, R
A13: A, T, G, C, N, D, S, R
A14: A, T, G, C, N, D, S, R
A15: A, T, G, C, N, D, S, R
A16: A, T, G, C, N, D, S, R
A17: A, T, G, C, N, D, S, R
A18: A, T, G, C, N, D, S, R
A19: A, T, G, C, N, D, S, R
A20: A, T, G, C, N, D, S, R
A21: A, T, G, C, N, D, S, R
A22: A, T, G, C, N, D, S, R
A23: A, T, G, C, N, D, S, R
A24: A, T, G, C, N, D, S, R
A25: A, T, G, C, N, D, S, R
A26: A, T, G, C, N, D, S, R
A27: A, T, G, C, N, D, S, R
A28: A, T, G, C, N, D, S, R
A29: A, T, G, C, N, D, S, R
A30: A, T, G, C, N, D, S, R
A31: A, T, G, C, N, D, S, R
A32: A, T, G, C, N, D, S, R
A33: A, T, G, C, N, D, S, R
A34: A, T, G, C, N, D, S, R
A35: A, T, G, C, N, D, S, R
A36: A, T, G, C, N, D, S, R
A37: A, T, G, C, N, D, S, R
A38: A, T, G, C, N, D, S, R
A39: A, T, G, C, N, D, S, R
A40: A, T, G, C, N, D, S, R
A41: A, T, G, C, N, D, S, R
A42: A, T, G, C, N, D, S, R
A43: A, T, G, C, N, D, S, R
A44: A, T, G, C, N, D, S, R
A45: A, T, G, C, N, D, S, R
A46: A, T, G, C, N, D, S, R
A47: A, T, G, C, N, D, S, R
A48: A, T, G, C, N, D, S, R
A49: A, T, G, C, N, D, S, R
A50: A, T, G, C, N, D, S, R
A51: A, T, G, C, N, D, S, R
A52: A, T, G, C, N, D, S, R
A53: A, T, G, C, N, D, S, R
A54: A, T, G, C, N, D, S, R
A55: A, T, G, C, N, D, S, R
A56: A, T, G, C, N, D, S, R
A57: A, T, G, C, N, D, S, R
A58: A, T, G, C, N, D, S, R
A59: A, T, G, C, N, D, S, R
