active ARFF CC-BY Visibility: public Uploaded 19-05-2021 by Meilina Reksoprodjo
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Author: Francesca Grisoni, Claudia S. Neuhaus, Miyabi Hishinuma, Gisela Gabernet, Jan A. Hiss, - Masaaki Kotera, Gisbert Schneider Source: [UCI]( - 2019 Please cite: [Paper]( Peptides with experimental annotations on their anticancer action on breast and lung cancer cells. This dataset only contains the breast cancer data. Membranolytic anticancer peptides (ACPs) are drawing increasing attention as potential future therapeutics against cancer, due to their ability to hinder the development of cellular resistance and their potential to overcome common hurdles of chemotherapy, e.g., side effects and cytotoxicity. This dataset contains information on peptides (annotated for their one-letter amino acid code) and their anticancer activity on breast and lung cancer cell lines. The final training sets contained 949 peptides for Breast cancer and 901 peptides for Lung cancer. ### Attribute Information: The dataset contains three attributes: 1. Peptide ID 2. One-letter amino-acid sequence 3. Class (active, moderately active, experimental inactive, virtual inactive)

3 features

IDnumeric949 unique values
0 missing
sequencestring949 unique values
0 missing
classstring4 unique values
0 missing

19 properties

Number of instances (rows) of the dataset.
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
Number of missing values in the dataset.
Number of instances with at least one value missing.
Number of numeric attributes.
Number of nominal attributes.
Percentage of binary attributes.
Percentage of instances having missing values.
Average class difference between consecutive instances.
Percentage of missing values.
Number of attributes divided by the number of instances.
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
Number of binary attributes.

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