p53 Mutants
Donated on 2/8/2010
The goal is to model mutant p53 transcriptional activity (active vs inactive) based on data extracted from biophysical simulations.
Dataset Characteristics
Multivariate
Subject Area
Biology
Associated Tasks
Classification
Feature Type
Real
# Instances
16772
# Features
5408
Dataset Information
Additional Information
Biophysical models of mutant p53 proteins yield features which can be used to predict p53 transcriptional activity. All class labels are determined via in vivo assays. K8.data - full dataset, 'K8' The following files are provided in order to reconstruct this historical subsets of this data set: K8.instance.tags - provides the precise p53 mutant tag for each instance in the K8.data, for use with the historical definition files: K1.def - defines instances in the 'K1' set. K2.def - defines instances in the 'K2' set. K3.def - defines instances in the 'K3' set. K4.def - defines instances in the 'K4' set. K5.def - defines instances in the 'K5' set. K6.def - defines instances in the 'K6' set. K7.def - defines instances in the 'K7' set. K8.def - defines instances in the 'K8' (full) set.
Has Missing Values?
Yes
Introductory Paper
By Samuel A. Danziger, Roberta Baronio, L. Ho, Linda Hall, K. Salmon, G. W. Hatfield, P. Kaiser, R. Lathrop. 2009
Published in PLoS Comput. Biol.
Variable Information
There are a total of 5409 attributes per instance. Attributes 1-4826 represent 2D electrostatic and surface based features. Attributes 4827-5408 represent 3D distance based features. Attribute 5409 is the class attribute, which is either active or inactive. The class labels are to be interpreted as follows: 'active' represents transcriptonally competent, active p53 whereas the 'inactive' label represents cancerous, inactive p53. Class labels are determined experimentally. More information is provided in the relevant papers cited.
Dataset Files
| File | Size |
|---|---|
| p53_new_2012.zip | 344.4 MB |
| p53_old_2010.zip | 182.6 MB |
| p53.names | 2.7 KB |
Reviews
There are no reviews for this dataset yet.
pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset p53_mutants = fetch_ucirepo(id=188) # data (as pandas dataframes) X = p53_mutants.data.features y = p53_mutants.data.targets # metadata print(p53_mutants.metadata) # variable information print(p53_mutants.variables)
Lathrop, R. (2009). p53 Mutants [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5T89H.
Creators
Richard Lathrop
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.