Data
molecular-biology_promoters

molecular-biology_promoters

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  • study_1 study_123 study_50 study_52 study_7 study_88 uci
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Author: C. Harley, R. Reynolds, M. Noordewier, J. Shavlik. Source: [UCI](https://archive.ics.uci.edu/ml/datasets/Molecular+Biology+(Promoter+Gene+Sequences)) - 1990 Please cite: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html) E. coli promoter gene sequences (DNA) Compilation of promoters with known transcriptional start points for E. coli genes. The task is to recognize promoters in strings that represent nucleotides (one of A, G, T, or C). A promoter is a genetic region which initiates the first step in the expression of an adjacent gene (transcription). The input features are 57 sequential DNA nucleotides. Fifty-three sample promoters and 53 nonpromoter sequences were used. The 53 sample promoters were obtained from a compilation produced by Hawley and McClure (1983). Negative training examples were thus derived by selecting contiguous substrings from a 1.5 kilobase sequence provided by Prof. T. Record of the Univ. of Wisconsin’s Chemistry Dept. This sequence is a fragment from E. coli bacteriophage T7 isolated with the restriction enzyme HaeIII. By virtue of the fact that the fragment does not bind RNA polymerase, it is believed to not contain any promoter sites. This dataset has been developed to help evaluate a "hybrid" learning algorithm ("KBANN") that uses examples to inductively refine preexisting knowledge. ### Attribute Description * 1. One of {+/-}, indicating the class ("+" = promoter). * 2. The instance name (non-promoters named by position in the 1500-long nucleotide sequence provided by T. Record). * 3-59. The remaining 57 fields are the sequence, starting at position -50 (p-50) and ending at position +7 (p7). Each of these fields is filled by one of {a, g, t, c}. ### Relevant papers * Harley, C. and Reynolds, R. 1987. "Analysis of E. Coli Promoter Sequences." Nucleic Acids Research, 15:2343-2361. * Towell, G., Shavlik, J. and Noordewier, M. 1990. "Refinement of Approximate Domain Theories by Knowledge-Based Artificial Neural Networks." In Proceedings of the Eighth National Conference on Artificial Intelligence (AAAI-90).

59 features

class (target)nominal2 unique values
0 missing
instance (row identifier)nominal106 unique values
0 missing
p-50nominal4 unique values
0 missing
p-49nominal4 unique values
0 missing
p-48nominal4 unique values
0 missing
p-47nominal4 unique values
0 missing
p-46nominal4 unique values
0 missing
p-45nominal4 unique values
0 missing
p-44nominal4 unique values
0 missing
p-43nominal4 unique values
0 missing
p-42nominal4 unique values
0 missing
p-41nominal4 unique values
0 missing
p-40nominal4 unique values
0 missing
p-39nominal4 unique values
0 missing
p-38nominal4 unique values
0 missing
p-37nominal4 unique values
0 missing
p-36nominal4 unique values
0 missing
p-35nominal4 unique values
0 missing
p-34nominal4 unique values
0 missing
p-33nominal4 unique values
0 missing
p-32nominal4 unique values
0 missing
p-31nominal4 unique values
0 missing
p-30nominal4 unique values
0 missing
p-29nominal4 unique values
0 missing
p-28nominal4 unique values
0 missing
p-27nominal4 unique values
0 missing
p-26nominal4 unique values
0 missing
p-25nominal4 unique values
0 missing
p-24nominal4 unique values
0 missing
p-23nominal4 unique values
0 missing
p-22nominal4 unique values
0 missing
p-21nominal4 unique values
0 missing
p-20nominal4 unique values
0 missing
p-19nominal4 unique values
0 missing
p-18nominal4 unique values
0 missing
p-17nominal4 unique values
0 missing
p-16nominal4 unique values
0 missing
p-15nominal4 unique values
0 missing
p-14nominal4 unique values
0 missing
p-13nominal4 unique values
0 missing
p-12nominal4 unique values
0 missing
p-11nominal4 unique values
0 missing
p-10nominal4 unique values
0 missing
p-9nominal4 unique values
0 missing
p-8nominal4 unique values
0 missing
p-7nominal4 unique values
0 missing
p-6nominal4 unique values
0 missing
p-5nominal4 unique values
0 missing
p-4nominal4 unique values
0 missing
p-3nominal4 unique values
0 missing
p-2nominal4 unique values
0 missing
p-1nominal4 unique values
0 missing
p1nominal4 unique values
0 missing
p2nominal4 unique values
0 missing
p3nominal4 unique values
0 missing
p4nominal4 unique values
0 missing
p5nominal4 unique values
0 missing
p6nominal4 unique values
0 missing
p7nominal4 unique values
0 missing

107 properties

106
Number of instances (rows) of the dataset.
59
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
0
Number of numeric attributes.
59
Number of nominal attributes.
30.63
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1
Number of binary attributes.
0.02
First quartile of mutual information between the nominal attributes and the target attribute.
0.29
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.57
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.26
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.85
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
3.97
Average number of distinct values among the attributes of the nominal type.
First quartile of skewness among attributes of the numeric type.
0.42
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.26
Standard deviation of the number of distinct values among attributes of the nominal type.
0.22
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
Mean skewness among attributes of the numeric type.
First quartile of standard deviation of attributes of the numeric type.
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.22
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.57
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
Mean standard deviation of attributes of the numeric type.
1.96
Second quartile (Median) of entropy among attributes.
0.29
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.57
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.16
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
50
Percentage of instances belonging to the most frequent class.
1.74
Minimal entropy among attributes.
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.42
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1
Entropy of the target attribute values.
0.68
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
53
Number of instances belonging to the most frequent class.
Minimum kurtosis among attributes of the numeric type.
Second quartile (Median) of means among attributes of the numeric type.
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.74
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
2
Maximum entropy among attributes.
Minimum of means among attributes of the numeric type.
0.03
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.29
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.25
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum kurtosis among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of skewness among attributes of the numeric type.
0.42
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.51
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum of means among attributes of the numeric type.
2
The minimal number of distinct values among attributes of the nominal type.
1.69
Percentage of binary attributes.
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.56
Number of attributes divided by the number of instances.
0.35
Maximum mutual information between the nominal attributes and the target attribute.
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
1.98
Third quartile of entropy among attributes.
0.37
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
16.25
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
4
The maximum number of distinct values among attributes of the nominal type.
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
Third quartile of kurtosis among attributes of the numeric type.
0.99
Average class difference between consecutive instances.
0.26
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.85
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Maximum skewness among attributes of the numeric type.
50
Percentage of instances belonging to the least frequent class.
0
Percentage of numeric attributes.
Third quartile of means among attributes of the numeric type.
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.22
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Maximum standard deviation of attributes of the numeric type.
53
Number of instances belonging to the least frequent class.
100
Percentage of nominal attributes.
0.07
Third quartile of mutual information between the nominal attributes and the target attribute.
0.22
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.37
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.57
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.95
Average entropy of the attributes.
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1.93
First quartile of entropy among attributes.
Third quartile of skewness among attributes of the numeric type.
0.57
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.26
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.85
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Mean kurtosis among attributes of the numeric type.
0.09
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of kurtosis among attributes of the numeric type.
Third quartile of standard deviation of attributes of the numeric type.
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.22
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Mean of means among attributes of the numeric type.
0.06
Average mutual information between the nominal attributes and the target attribute.
0.81
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of means among attributes of the numeric type.
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.22
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.37
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.57
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

18 tasks

105 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
31 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: p7
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: p7
0 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: p7
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
2 runs - estimation_procedure: Interleaved Test then Train - target_feature: p7
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
0 runs - estimation_procedure: Interleaved Test then Train - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
Define a new task