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Uni Köln
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Datenportal des Lehrstuhls für Statistik und Ökonometrie
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Ecoli (CP vs PP) data
The data set (and description) can be downloaded here:
http://archive.ics.uci.edu/ml/machine-learning-databases/ecoli/ecoli.data
Description:
1. Title: Protein Localization Sites
2. Creator and Maintainer:
Kenta Nakai
Institue of Molecular and Cellular Biology
Osaka, University
1-3 Yamada-oka, Suita 565 Japan
nakai@imcb.osaka-u.ac.jp
http://www.imcb.osaka-u.ac.jp/nakai/psort.html
Donor: Paul Horton (paulh@cs.berkeley.edu)
Date: September, 1996
See also: yeast database
3. Past Usage.
Reference: "A Probablistic Classification System for Predicting the Cellular
Localization Sites of Proteins", Paul Horton & Kenta Nakai,
Intelligent Systems in Molecular Biology, 109-115.
St. Louis, USA 1996.
Results: 81% for E.coli with an ad hoc structured
probability model. Also similar accuracy for Binary Decision Tree and
Bayesian Classifier methods applied by the same authors in
unpublished results.
Predicted Attribute: Localization site of protein. ( non-numeric ).
4. The references below describe a predecessor to this dataset and its
development. They also give results (not cross-validated) for classification
by a rule-based expert system with that version of the dataset.
Reference: "Expert Sytem for Predicting Protein Localization Sites in
Gram-Negative Bacteria", Kenta Nakai & Minoru Kanehisa,
PROTEINS: Structure, Function, and Genetics 11:95-110, 1991.
Reference: "A Knowledge Base for Predicting Protein Localization Sites in
Eukaryotic Cells", Kenta Nakai & Minoru Kanehisa,
Genomics 14:897-911, 1992.
5. Number of Instances: 336 for the E.coli dataset and
6. Number of Attributes.
for E.coli dataset: 8 ( 7 predictive, 1 name )
7. Attribute Information.
1. Sequence Name: Accession number for the SWISS-PROT database
2. mcg: McGeoch's method for signal sequence recognition.
3. gvh: von Heijne's method for signal sequence recognition.
4. lip: von Heijne's Signal Peptidase II consensus sequence score.
Binary attribute.
5. chg: Presence of charge on N-terminus of predicted lipoproteins.
Binary attribute.
6. aac: score of discriminant analysis of the amino acid content of
outer membrane and periplasmic proteins.
7. alm1: score of the ALOM membrane spanning region prediction program.
8. alm2: score of ALOM program after excluding putative cleavable signal
regions from the sequence.
8. Missing Attribute Values: None.
9. Class Distribution. The class is the localization site. Please see Nakai &
Kanehisa referenced above for more details.
cp (cytoplasm) 143
im (inner membrane without signal sequence) 77
pp (perisplasm) 52
imU (inner membrane, uncleavable signal sequence) 35
om (outer membrane) 20
omL (outer membrane lipoprotein) 5
imL (inner membrane lipoprotein) 2
imS (inner membrane, cleavable signal sequence) 2
Citation Request:
Please refer to the repository http://archive.ics.uci.edu/ml (see citation policy).
See also Frank, A. & Asuncion, A. (2010). UCI Machine Learning Repository
[http://archive.ics.uci.edu/ml].
Irvine, CA: University of California, School of Information and Computer Science.
Descriptive statistics:
Dataset= ecoli_cpvspp : n= 195 , d= 5
Class1: n= 143
Covariance matrix:
[,1] [,2] [,3] [,4] [,5]
[1,] 0.0153 0.0027 -0.0009 0.0006 -0.0008
[2,] 0.0027 0.0080 -0.0012 0.0022 0.0012
[3,] -0.0009 -0.0012 0.0077 -0.0008 -0.0004
[4,] 0.0006 0.0022 -0.0008 0.0098 0.0085
[5,] -0.0008 0.0012 -0.0004 0.0085 0.0092
Correlation matrix:
[,1] [,2] [,3] [,4] [,5]
[1,] 1.0000 0.2397 -0.0855 0.0478 -0.0636
[2,] 0.2397 1.0000 -0.1579 0.2435 0.1382
[3,] -0.0855 -0.1579 1.0000 -0.0893 -0.0445
[4,] 0.0478 0.2435 -0.0893 1.0000 0.8957
[5,] -0.0636 0.1382 -0.0445 0.8957 1.0000
Median: 0.3597 0.4085 0.4512 0.3117 0.3976
Mean: 0.3636 0.4097 0.4545 0.3127 0.3952
MCD-estimated:
MDC-0.975-Mean: 0.3454 0.4056 0.4542 0.321 0.4155
MDC-0.750-Mean: 0.3454 0.4056 0.4542 0.321 0.4155
MDC-0.500-Mean: 0.3454 0.4056 0.4542 0.321 0.4155
Class2: n= 52
Covariance matrix:
[,1] [,2] [,3] [,4] [,5]
[1,] 0.0082 0.0049 -0.0017 0.0035 -0.0011
[2,] 0.0049 0.0167 -0.0046 0.0008 -0.0012
[3,] -0.0017 -0.0046 0.0073 -0.0006 0.0012
[4,] 0.0035 0.0008 -0.0006 0.0102 0.0040
[5,] -0.0011 -0.0012 0.0012 0.0040 0.0140
Correlation matrix:
[,1] [,2] [,3] [,4] [,5]
[1,] 1.0000 0.4169 -0.2151 0.3791 -0.1011
[2,] 0.4169 1.0000 -0.4196 0.0649 -0.0761
[3,] -0.2151 -0.4196 1.0000 -0.0658 0.1182
[4,] 0.3791 0.0649 -0.0658 1.0000 0.3309
[5,] -0.1011 -0.0761 0.1182 0.3309 1.0000
Median: 0.6617 0.7065 0.4372 0.4572 0.3642
Mean: 0.6521 0.6998 0.4367 0.4681 0.3744
MCD-estimated:
MDC-0.975-Mean: 0.6726 0.7238 0.4279 0.4633 0.3636
MDC-0.750-Mean: 0.6726 0.7238 0.4279 0.4633 0.3636
MDC-0.500-Mean: 0.6724 0.7213 0.4292 0.4584 0.3584
Measures:
Mah.Dist: 3.7929
Mah.Dist-MCD-0.975: 4.6429
Mah.Dist-MCD-0.750: 4.6949
Mah.Dist-MCD-0.500: 4.6429
Zuletzt geändert am 17.02.2013
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