资源介绍
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)
See also: yeast database
Data Set Information:
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. 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.
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]
Papers That Cite This Data Set1:
Vassilis Athitsos and Stan Sclaroff. Boosting Nearest Neighbor Classifiers for Multiclass Recognition. Boston University Computer Science Tech. Report No, 2004-006. 2004. [View Context].
Charles X. Ling and Qiang Yang and Jianning Wang and Shichao Zhang. Decision trees with minimal costs. ICML. 2004. [View Context].
Xiaoyong Chai and Li Deng and Qiang Yang and Charles X. Ling. Test-Cost Sensitive Naive Bayes Classification. ICDM. 2004. [View Context].
Aik Choon Tan and David Gilbert. An Empirical Comparison of Supervised Machine Learning Techniques in Bioinformatics. APBC. 2003. [View Context].
Mukund Deshpande and George Karypis. Evaluation of Techniques for Classifying Biological Sequences. PAKDD. 2002. [View Context].
Huajie Zhang and Charles X. Ling. An Improved Learning Algorithm for Augmented Naive Bayes. PAKDD. 2001. [View Context].
Mark A. Hall.
