Data
molecular-biology_promoters

molecular-biology_promoters

active ARFF Publicly available Visibility: public Uploaded 23-04-2014 by Jan van Rijn
0 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes
  • Life Science Machine Learning study_1 study_123 study_50 study_52 study_7 study_88 uci
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
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).

58 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
Show all 58 features

107 properties

NumberOfInstances
106
Number of instances (rows) of the dataset.
NumberOfFeatures
58
Number of attributes (columns) of the dataset.
NumberOfClasses
2
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
0
Number of numeric attributes.
NumberOfSymbolicFeatures
58
Number of nominal attributes.
Quartile2SkewnessOfNumericAtts
Second quartile (Median) of skewness among attributes of the numeric type.
REPTreeDepth3Kappa
0.42
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
DecisionStumpKappa
0.51
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
MaxMeansOfNumericAtts
Maximum of means among attributes of the numeric type.
MinMutualInformation
0
Minimal mutual information between the nominal attributes and the target attribute.
PercentageOfBinaryFeatures
1.72
Percentage of binary attributes.
Quartile2StdDevOfNumericAtts
Second quartile (Median) of standard deviation of attributes of the numeric type.
RandomTreeDepth1AUC
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Dimensionality
0.55
Number of attributes divided by the number of instances.
MaxMutualInformation
0.35
Maximum mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
2
The minimal number of distinct values among attributes of the nominal type.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
Quartile3AttributeEntropy
1.98
Third quartile of entropy among attributes.
RandomTreeDepth1ErrRate
0.37
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
EquivalentNumberOfAtts
16.25
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
MaxNominalAttDistinctValues
4
The maximum number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
Minimum skewness among attributes of the numeric type.
PercentageOfMissingValues
0
Percentage of missing values.
Quartile3KurtosisOfNumericAtts
Third quartile of kurtosis among attributes of the numeric type.
AutoCorrelation
0.99
Average class difference between consecutive instances.
RandomTreeDepth1Kappa
0.26
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
J48.00001.AUC
0.85
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
MaxSkewnessOfNumericAtts
Maximum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
Minimum standard deviation of attributes of the numeric type.
PercentageOfNumericFeatures
0
Percentage of numeric attributes.
Quartile3MeansOfNumericAtts
Third quartile of means among attributes of the numeric type.
CfsSubsetEval_DecisionStumpAUC
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
RandomTreeDepth2AUC
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
J48.00001.ErrRate
0.22
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
MaxStdDevOfNumericAtts
Maximum standard deviation of attributes of the numeric type.
MinorityClassPercentage
50
Percentage of instances belonging to the least frequent class.
PercentageOfSymbolicFeatures
100
Percentage of nominal attributes.
Quartile3MutualInformation
0.07
Third quartile of mutual information between the nominal attributes and the target attribute.
CfsSubsetEval_DecisionStumpErrRate
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
RandomTreeDepth2ErrRate
0.37
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
J48.00001.Kappa
0.57
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
MeanAttributeEntropy
1.95
Average entropy of the attributes.
MinorityClassSize
53
Number of instances belonging to the least frequent class.
Quartile1AttributeEntropy
1.93
First quartile of entropy among attributes.
Quartile3SkewnessOfNumericAtts
Third quartile of skewness among attributes of the numeric type.
CfsSubsetEval_DecisionStumpKappa
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
RandomTreeDepth2Kappa
0.26
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
J48.0001.AUC
0.85
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
MeanKurtosisOfNumericAtts
Mean kurtosis among attributes of the numeric type.
NaiveBayesAUC
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
Quartile1KurtosisOfNumericAtts
First quartile of kurtosis among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
Third quartile of standard deviation of attributes of the numeric type.
CfsSubsetEval_NaiveBayesAUC
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
RandomTreeDepth3AUC
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
J48.0001.ErrRate
0.22
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
MeanMeansOfNumericAtts
Mean of means among attributes of the numeric type.
NaiveBayesErrRate
0.09
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
Quartile1MeansOfNumericAtts
First quartile of means among attributes of the numeric type.
REPTreeDepth1AUC
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
CfsSubsetEval_NaiveBayesErrRate
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
RandomTreeDepth3ErrRate
0.37
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
J48.0001.Kappa
0.57
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
MeanMutualInformation
0.06
Average mutual information between the nominal attributes and the target attribute.
NaiveBayesKappa
0.81
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
Quartile1MutualInformation
0.02
First quartile of mutual information between the nominal attributes and the target attribute.
REPTreeDepth1ErrRate
0.29
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
CfsSubsetEval_NaiveBayesKappa
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
RandomTreeDepth3Kappa
0.26
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
J48.001.AUC
0.85
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
MeanNoiseToSignalRatio
30.63
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
NumberOfBinaryFeatures
1
Number of binary attributes.
Quartile1SkewnessOfNumericAtts
First quartile of skewness among attributes of the numeric type.
REPTreeDepth1Kappa
0.42
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
CfsSubsetEval_kNN1NAUC
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
StdvNominalAttDistinctValues
0.26
Standard deviation of the number of distinct values among attributes of the nominal type.
J48.001.ErrRate
0.22
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
MeanNominalAttDistinctValues
3.97
Average number of distinct values among the attributes of the nominal type.
Quartile1StdDevOfNumericAtts
First quartile of standard deviation of attributes of the numeric type.
REPTreeDepth2AUC
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
CfsSubsetEval_kNN1NErrRate
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
kNN1NAUC
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
J48.001.Kappa
0.57
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MeanSkewnessOfNumericAtts
Mean skewness among attributes of the numeric type.
Quartile2AttributeEntropy
1.96
Second quartile (Median) of entropy among attributes.
REPTreeDepth2ErrRate
0.29
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
CfsSubsetEval_kNN1NKappa
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
kNN1NErrRate
0.16
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
MajorityClassPercentage
50
Percentage of instances belonging to the most frequent class.
MeanStdDevOfNumericAtts
Mean standard deviation of attributes of the numeric type.
Quartile2KurtosisOfNumericAtts
Second quartile (Median) of kurtosis among attributes of the numeric type.
REPTreeDepth2Kappa
0.42
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
ClassEntropy
1
Entropy of the target attribute values.
kNN1NKappa
0.68
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
MajorityClassSize
53
Number of instances belonging to the most frequent class.
MinAttributeEntropy
1.74
Minimal entropy among attributes.
Quartile2MeansOfNumericAtts
Second quartile (Median) of means among attributes of the numeric type.
REPTreeDepth3AUC
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
DecisionStumpAUC
0.74
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
MaxAttributeEntropy
2
Maximum entropy among attributes.
MinKurtosisOfNumericAtts
Minimum kurtosis among attributes of the numeric type.
Quartile2MutualInformation
0.03
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
REPTreeDepth3ErrRate
0.29
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
DecisionStumpErrRate
0.25
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
MaxKurtosisOfNumericAtts
Maximum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
Minimum of means among attributes of the numeric type.
Show all 107 properties

29 tasks

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