机器学习_Yeast Data Set(酵母数据集)

Yeast Data Set(酵母数据集)

数据摘要：

Predicting the Cellular Localization Sites of Proteins

中文关键词：

多变量,分类,UCI,酵母,

英文关键词：

Multivariate,Classification,UCI,Yeast,

数据格式：

TEXT

数据用途：

This data set is used for classification.

数据详细介绍：

Yeast Data Set Abstract: Predicting the Cellular Localization Sites of Proteins

Source:

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 '@' https://www.sodocs.net/doc/d119024136.html,)

Data Set Information:

Predicted Attribute: Localization site of protein. ( non-numeric ).

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.

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. alm: Score of the ALOM membrane spanning region prediction program.

5. mit: Score of discriminant analysis of the amino acid content of the N-terminal region (20 residues long) of mitochondrial and non-mitochondrial proteins.

6. erl: Presence of "HDEL" substring (thought to act as a signal for retention in the endoplasmic reticulum lumen). Binary attribute.

7. pox: Peroxisomal targeting signal in the C-terminus.

8. vac: Score of discriminant analysis of the amino acid content of vacuolar and extracellular proteins.

9. nuc: Score of discriminant analysis of nuclear localization signals of nuclear and

non-nuclear proteins.

Relevant Papers:

Paul Horton & Kenta Nakai, "A Probablistic Classification System for Predicting the Cellular Localization Sites of Proteins", Intelligent Systems in Molecular Biology, 109-115. St. Louis, USA 1996.

[Web Link]

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:

Kenta Nakai & Minoru Kanehisa, "Expert Sytem for Predicting Protein Localization Sites in Gram-Negative Bacteria", PROTEINS: Structure, Function, and Genetics 11:95-110, 1991.

Kenta Nakai & Minoru Kanehisa, "A Knowledge Base for Predicting Protein Localization Sites in Eukaryotic Cells", Genomics 14:897-911, 1992.

[Web Link]

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