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data/tanveer/molec-biol-splice/splice.names

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+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%)
+