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qsfsr2

qsfsr2

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Author: Source: Unknown - Date unknown Please cite: This is one of 41 drug design datasets. The datasets with 1143 features are formed using Adriana.Code software (www.molecular-networks.com/software/adrianacode). The molecules and outputs are taken from the original studies (see below). The other datasets are taken exactly from the original studies. The last attribute in each file is the target. Original studies: carbolenes "B. D. Silverman and Daniel. E. Platt, J. Med. Chem. 1996, 39, 2129-2140" mtp2 "Bergstrom, C. A. S.; Norinder, U.; Luthman, K.; Artursson, P. Molecular Descriptors Influencing Melting Point and Their Role in Classification of Solid Drugs. J. Chem. Inf. Comput. Sci.; (Article); 2003; 43(4); 1177-1185" chang, cristalli, depreux, doherty, garrat2, garrat, heyl, krystek, lewis, penning, rosowsky, siddiqi, stevenson, strupcz, svensson, thompson, tsutumi, uejling, yokoyama1, yokoyama2 "David E Patterson, Richard D Cramer, Allan M Ferguson, Robert D Clark, Laurence W Weinberger. Neighbourhood Behaviour: A Useful Concept for Validation of ""Molecular Diversity"" Descriptors. J. Med. Chem. 1996 (39) 3049 - 3059." mtp "Karthikeyan, M.; Glen, R.C.; Bender, A. General melting point prediction based on a diverse compound dataset and artificial neural networks. J. Chem. Inf. Model.; 2005; 45(3); 581-590" benzo32 "Harrison,P.W. and Barlin,G.B. and Davies,L.P. and Ireland,S.J. and Matyus,P. and Wong,M.G., Syntheses, pharmacological evaluation and molecular modelling of substituted 6-alkoxyimidazo[1,2-b]pyridazines as new ligands for the benzodiazepine receptor, European Journal of Medicinal Chemistry, (31), 1996, 651-662" PHENETYL1 "H. Kubinyi (Ed.): ""QSAR: Hansch Analysis and Related Approaches"", VCH, Weinhein (Ger), 1993, pp.57-68" pah "Todeschini, R.; Gramatica, P.; Marengo, E.; Provenzani, R. Weighted Holistic Invariant Molecular Descriptors. Part 2. Theory Development and Applications on Modeling Physico-Chemical Properties of PolyAromatic Hydrocarbons (PAH). Chemom. Intell. Lab. Syst. 1995, 27, 221-229." pdgfr "R. Guha and P. Jurs. The Development of Linear, Ensemble and Non-linear Models for the Prediction and Interpretation of the Biological Activity of a Set of PDGFR Inhibitors. J. Chem. Inf. Comput. Sci. 2004, 44 (6), 2179-2189" Phen "Cammarata, A. Interrelationship of the Regression Models Used for Structure-Activity Analyses. J. Med. Chem. 1972, 15, 573-577" topo_2_1, yprop_4_1 "Jun Feng et al, Predictive Toxicology: Benchmarking Molecular Descriptors and Statistical Methods, J. Chem. Inf Comput. Sci., 2003 (43) 1463-1470" qsabr1, qsabr2 "Damborsky, J., Schultz, T.W., Comparison of the QSAR models for toxicity and biodegradability of anilines and phenols, Chemosphere 34: 429-446, 1997" qsartox "Blaha, L., Damborsky, J., Nemec, M., QSAR for acute toxicity of saturated and unsaturated halogenated aliphatic compounds, Chemosphere 36: 1345-1365, 1998" qsbr_rw1 "Damborsky, J. et al., Structure-biodegradability relationships for chlorinated dibenzo-p-dioxins and dibenzofurans, In: Wittich, R.-M., Biodegradation of dioxins and furans, R.G. Landes Company, Austin, 1998" qsbr_y2 "Damborsky, J. et al., A mechanistic approach to deriving QSBR- A case study: dehalogenation of haloaliphatic compounds, In: Peijnenburg, W.J.G.M., Damborsky, J., Biodegradability Prediction, Kluwer Academic Publishers" qsbralks "Damborsky, J. et al., Mechanism-based Quantitative Structure-Biodegradability Relationships for hydrolytic dehalogenation of chloro- and bromo-alkenes, Quantitative Structure-Activity Relationships 17: 450-458, 1998" qsfrdhla "Damborsky, J., Quantitative structure-function relationships of the single-point mutants of haloalkane dehalogenase: A multivariate approach, Qunatitative Structure-Activity Relationships 16: 126-135, 1997" qsfsr1 "Damborsky, J., Quantitative structure-function and structure-stability relationships of purposely modified proteins, Protein Engineering 11: 21-30, 1998" qsfsr2 "Damborsky, J., Quantitative structure-function and structure-stability relationships of purposely modified proteins, Protein Engineering 11: 21-30, 1998" qsprcmpx "Cajan, M. et al., Stability of Aromatic Amides with Bromide Anion: Quantitative Structure-Property Relationships, Journal of Chemical Information and Computer Sciences, in press, 2000" selwood "Selwood, D. L.; Livingstone, D. J.; Comley, J. C.; O'Dowd, A. B.; Hudson, A. T.; Jackson, P.; Jandu, K. S.; Rose, V. S.; Stables, J. N. Structure-Activity Relationships of Antifilarial Antimycin Analogues: A Multivariate Pattern Recognition Study J. Med. Chem., 1990, 33, 136-142"

10 features

oz10 (target)numeric15 unique values
0 missing
oz1numeric19 unique values
0 missing
oz2numeric16 unique values
0 missing
oz3numeric17 unique values
0 missing
oz4numeric16 unique values
0 missing
oz5numeric19 unique values
0 missing
oz6numeric8 unique values
0 missing
oz7numeric18 unique values
0 missing
oz8numeric15 unique values
0 missing
oz9numeric18 unique values
0 missing

107 properties

19
Number of instances (rows) of the dataset.
10
Number of attributes (columns) of the dataset.
0
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.
10
Number of numeric attributes.
0
Number of nominal attributes.
0.74
Average class difference between consecutive instances.
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
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
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
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
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
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
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
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
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
Entropy of the target attribute values.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.53
Number of attributes divided by the number of instances.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
Percentage of instances belonging to the most frequent class.
Number of instances belonging to the most frequent class.
Maximum entropy among attributes.
4.17
Maximum kurtosis among attributes of the numeric type.
0.76
Maximum of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
The maximum number of distinct values among attributes of the nominal type.
1.62
Maximum skewness among attributes of the numeric type.
0.34
Maximum standard deviation of attributes of the numeric type.
Average entropy of the attributes.
0.67
Mean kurtosis among attributes of the numeric type.
0.48
Mean of means among attributes of the numeric type.
Average mutual information between the nominal attributes and the target attribute.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Average number of distinct values among the attributes of the nominal type.
0.09
Mean skewness among attributes of the numeric type.
0.28
Mean standard deviation of attributes of the numeric type.
Minimal entropy among attributes.
-1.38
Minimum kurtosis among attributes of the numeric type.
0.27
Minimum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
The minimal number of distinct values among attributes of the nominal type.
-1.98
Minimum skewness among attributes of the numeric type.
0.22
Minimum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
100
Percentage of numeric attributes.
0
Percentage of nominal attributes.
First quartile of entropy among attributes.
-0.97
First quartile of kurtosis among attributes of the numeric type.
0.37
First quartile of means among attributes of the numeric type.
First quartile of mutual information between the nominal attributes and the target attribute.
-0.39
First quartile of skewness among attributes of the numeric type.
0.26
First quartile of standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.2
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.47
Second quartile (Median) of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.17
Second quartile (Median) of skewness among attributes of the numeric type.
0.27
Second quartile (Median) of standard deviation of attributes of the numeric type.
Third quartile of entropy among attributes.
2.59
Third quartile of kurtosis among attributes of the numeric type.
0.59
Third quartile of means among attributes of the numeric type.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.58
Third quartile of skewness among attributes of the numeric type.
0.31
Third quartile of standard deviation of attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
Standard deviation of the number of distinct values among attributes of the nominal type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk

13 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: oz10
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: oz10
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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