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  data_files:
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  - split: train
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  path: data/train-*
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  data_files:
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  - split: train
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  path: data/train-*
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+ license: cc-by-4.0
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+ tags:
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+ - biology
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+ - chemistry
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+ - medical
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  ---
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+ # Dataset Card for Dataset Name
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+
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+ <!-- Provide a quick summary of the dataset. -->
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+
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+ This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
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+
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+ ## Dataset Details
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+
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+ ### Dataset Description
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+
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+ <!-- Provide a longer summary of what this dataset is. -->
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+ - **Curated by:** [More Information Needed]
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+ - **Funded by [optional]:** [More Information Needed]
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+ - **Shared by [optional]:** [More Information Needed]
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+ - **Language(s) (NLP):** [More Information Needed]
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+ - **License:** [More Information Needed]
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+
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+ ### Dataset Sources [optional]
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+
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+ <!-- Provide the basic links for the dataset. -->
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+ - **Repository:** [More Information Needed]
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+ - **Paper [optional]:** [More Information Needed]
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+ - **Demo [optional]:** [More Information Needed]
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+
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+ ## Uses
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+
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+ <!-- Address questions around how the dataset is intended to be used. -->
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+
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+ ### Direct Use
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+ <!-- This section describes suitable use cases for the dataset. -->
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+ [More Information Needed]
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+
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+ ### Out-of-Scope Use
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+ <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
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+ [More Information Needed]
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+
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+ ## Dataset Structure
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+
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+ <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
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+ [More Information Needed]
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+
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+ ## Dataset Creation
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+
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+ ### Curation Rationale
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+ <!-- Motivation for the creation of this dataset. -->
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+ [More Information Needed]
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+ ### Source Data
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+ <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
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+
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+ #### Data Collection and Processing
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+ <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
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+ [More Information Needed]
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+ #### Who are the source data producers?
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+ <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
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+ [More Information Needed]
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+ ### Annotations [optional]
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+ <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
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+
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+ #### Annotation process
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+ <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
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+ [More Information Needed]
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+ #### Who are the annotators?
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+ <!-- This section describes the people or systems who created the annotations. -->
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+ [More Information Needed]
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+ #### Personal and Sensitive Information
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+ <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
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+ [More Information Needed]
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+ ## Bias, Risks, and Limitations
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+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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+ [More Information Needed]
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+ ### Recommendations
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+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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+ Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
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+ ## Citation [optional]
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+ **BibTeX:**
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+ ```txt
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+ @article{
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+ doi:10.1126/sciadv.abm6638,
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+ author = {Xu Feng and Mengfan Tang and Merve Dede and Dan Su and Guangsheng Pei and Dadi Jiang and Chao Wang and Zhen Chen and Mi Li and Litong Nie and Yun Xiong and Siting Li and Jeong-Min Park and Huimin Zhang and Min Huang and Klaudia Szymonowicz and Zhongming Zhao and Traver Hart and Junjie Chen },
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+ title = {Genome-wide CRISPR screens using isogenic cells reveal vulnerabilities conferred by loss of tumor suppressors},
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+ journal = {Science Advances},
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+ volume = {8},
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+ number = {19},
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+ pages = {eabm6638},
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+ year = {2022},
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+ doi = {10.1126/sciadv.abm6638},
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+ URL = {https://www.science.org/doi/abs/10.1126/sciadv.abm6638},
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+ eprint = {https://www.science.org/doi/pdf/10.1126/sciadv.abm6638},
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+ abstract = {Exploiting cancer vulnerabilities is critical for the discovery of anticancer drugs. However, tumor suppressors cannot be directly targeted because of their loss of function. To uncover specific vulnerabilities for cells with deficiency in any given tumor suppressor(s), we performed genome-scale CRISPR loss-of-function screens using a panel of isogenic knockout cells we generated for 12 common tumor suppressors. Here, we provide a comprehensive and comparative dataset for genetic interactions between the whole-genome protein-coding genes and a panel of tumor suppressor genes, which allows us to uncover known and new high-confidence synthetic lethal interactions. Mining this dataset, we uncover essential paralog gene pairs, which could be a common mechanism for interpreting synthetic lethality. Moreover, we propose that some tumor suppressors could be targeted to suppress proliferation of cells with deficiency in other tumor suppressors. This dataset provides valuable information that can be further exploited for targeted cancer therapy. Whole-genome CRISPR screens uncover synthetic lethal interactions for tumor suppressors.}
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+ }
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+ ```
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+ ## Glossary [optional]
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+ <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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+ [More Information Needed]
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+ ## More Information [optional]
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+ [More Information Needed]
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+ ## Dataset Card Authors [optional]
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+ [More Information Needed]
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+ ## Dataset Card Contact
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+ [More Information Needed]