Datasets:
dataset_info:
features:
- name: GENE
dtype: string
- name: 293A_WT_T22_AB_XF498
dtype: float64
- name: 293A_LKB1_T22_AB
dtype: float64
- name: 293A_PTEN_T22_AB
dtype: float64
- name: 293A_VHL_T22_AB
dtype: float64
- name: 293A_BAP1NUMBER2_16_T25_AB
dtype: float64
- name: 293A_CDH1NUMBER2_15_T24_AB
dtype: float64
- name: 293A_NF2NUMBER2_3_T24_AB
dtype: float64
- name: 293A_WT_T21_AB_XF646
dtype: float64
- name: 293A_PBRM1_T25_AB
dtype: float64
- name: 293A_SETD2_T24_AB
dtype: float64
- name: 293A_WT_T20_AB_XF804
dtype: float64
- name: 293A_ARID1A_T21_AB
dtype: float64
- name: 293A_NF1_T24_AB
dtype: float64
- name: 293A_RB1_T21_AB
dtype: float64
- name: 293A_TP53_T21_AB
dtype: float64
- name: 293A_WT_T21_AB_XF821
dtype: float64
- name: 293A_KEAP1_T22_AB
dtype: float64
- name: 293A_g53BP1#1_T22_AB
dtype: float64
splits:
- name: train
num_bytes: 2770271
num_examples: 18053
download_size: 2401422
dataset_size: 2770271
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: cc-by-4.0
tags:
- biology
- chemistry
- medical
Dataset Card for Dataset Name
crispr-qbf-master: Master_QBF_293A_Xu_Feng_Tm_Sup_scr_042320
Dataset Details
Dataset Description
This dataset contains the results of genome-wide CRISPR screens using isogenic knockout cells to uncover vulnerabilities in tumor suppressor-deficient cancer cells. The data was originally published by Feng et al., Sci. Adv. 8, eabm6638 (2022) and is available on Figshare.
- Curated by: Feng et al., Sci. Adv. 8, eabm6638 (2022)
- Funded by: Not explicitly specified, but likely supported by institutions associated with the authors.
- Shared by: Feng et al.
- Language(s) (NLP): Not applicable (this is a biomedical dataset).
- License: CC BY 4.0
Dataset Sources [optional]
- Repository: Figshare - Feng, Tang, Dede et al. 2022
- Paper: Sci. Adv. 8, eabm6638 (2022)
Uses
Direct Use
This dataset can be used for identifying genetic dependencies and vulnerabilities in cancer research, especially related to tumor suppressor genes. Potential applications include:
- Identification of potential therapeutic targets.
- Understanding genetic interactions in cancer progression.
- Training machine learning models for genomic data analysis.
Out-of-Scope Use
This dataset should not be used for:
- Applications outside of research without proper domain expertise.
- Misinterpretation of the results to derive clinical conclusions without appropriate validation.
- Malicious use to generate unverified claims about genetic predispositions.
Dataset Structure
[More Information Needed]
Splits
- Train: Contains the entirety of the dataset for analysis. No explicit validation or test splits are provided.
Dataset Creation
Curation Rationale
Confirm the methodology behind the binary essentiality calls in Genome-wide CRISPR Screens Using Isogenic Cells Reveal Vulnerabilities Conferred by Loss of Tumor Suppressors manuscript by Feng et al.
[More Information Needed]
Source Data
Data Collection and Processing
Binary_essentiality_calls_analysis_Feng_et_al
[More Information Needed]
Who are the source data producers?
[More Information Needed]
Annotations [optional]
Annotation process
[More Information Needed]
Who are the annotators?
[More Information Needed]
Personal and Sensitive Information
[More Information Needed]
Bias, Risks, and Limitations
[More Information Needed]
Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
Citation [optional]
BibTeX:
@article{
Hart2022,
author = "Traver Hart and Merve Dede",
title = "{Feng, Tang, Dede et al 2022}",
year = "2022",
month = "3",
url = "https://figshare.com/articles/dataset/Feng_Tang_Dede_et_al_2022/19398332",
doi = "10.6084/m9.figshare.19398332.v1"
}
Glossary [optional]
[More Information Needed]
More Information [optional]
[More Information Needed]
Dataset Card Authors [optional]
[More Information Needed]
Dataset Card Contact
dwb2023