Datasets:
msr_genomics_kbcomp

	---
	annotations_creators:
	- expert-generated
	language_creators:
	- expert-generated
	language:
	- en
	license:
	- other
	multilinguality:
	- monolingual
	size_categories:
	- 10K<n<100K
	source_datasets:
	- original
	task_categories:
	- other
	task_ids: []
	pretty_name: MsrGenomicsKbcomp
	tags:
	- genomics-knowledge-base-bompletion
	dataset_info:
	features:
	- name: GENE1
	dtype: string
	- name: relation
	dtype:
	class_label:
	names:
	'0': Positive_regulation
	'1': Negative_regulation
	'2': Family
	- name: GENE2
	dtype: string
	splits:
	- name: train
	num_bytes: 256789
	num_examples: 12160
	- name: test
	num_bytes: 58116
	num_examples: 2784
	- name: validation
	num_bytes: 27457
	num_examples: 1315
	download_size: 0
	dataset_size: 342362
	---

	# Dataset Card for [Dataset Name]

	## Table of Contents
	- [Dataset Description](#dataset-description)
	- [Dataset Summary](#dataset-summary)
	- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
	- [Languages](#languages)
	- [Dataset Structure](#dataset-structure)
	- [Data Instances](#data-instances)
	- [Data Fields](#data-fields)
	- [Data Splits](#data-splits)
	- [Dataset Creation](#dataset-creation)
	- [Curation Rationale](#curation-rationale)
	- [Source Data](#source-data)
	- [Annotations](#annotations)
	- [Personal and Sensitive Information](#personal-and-sensitive-information)
	- [Considerations for Using the Data](#considerations-for-using-the-data)
	- [Social Impact of Dataset](#social-impact-of-dataset)
	- [Discussion of Biases](#discussion-of-biases)
	- [Other Known Limitations](#other-known-limitations)
	- [Additional Information](#additional-information)
	- [Dataset Curators](#dataset-curators)
	- [Licensing Information](#licensing-information)
	- [Citation Information](#citation-information)
	- [Contributions](#contributions)

	## Dataset Description

	- Homepage: [NCI-PID-PubMed Genomics Knowledge Base Completion Dataset](https://msropendata.com/datasets/80b4f6e8-5d7c-4abc-9c79-2e51dfedd791)
	- Repository: [NCI-PID-PubMed Genomics Knowledge Base Completion Dataset](NCI-PID-PubMed Genomics Knowledge Base Completion Dataset)
	- Paper: [Compositional Learning of Embeddings for Relation Paths in Knowledge Base and Text](https://www.aclweb.org/anthology/P16-1136/)
	- Point of Contact: [Kristina Toutanova](mailto:kristout@google.com)


	### Dataset Summary

	The database is derived from the NCI PID Pathway Interaction Database, and the textual mentions are extracted from cooccurring pairs of genes in PubMed abstracts, processed and annotated by Literome (Poon et al. 2014). This dataset was used in the paper “Compositional Learning of Embeddings for Relation Paths in Knowledge Bases and Text” (Toutanova, Lin, Yih, Poon, and Quirk, 2016). More details can be found in the included README.


	### Supported Tasks and Leaderboards

	[More Information Needed]

	### Languages

	English

	## Dataset Structure

	NCI-PID-PubMed Genomics Knowledge Base Completion Dataset

	This dataset includes a database of regulation relationships among genes and corresponding textual mentions of pairs of genes in PubMed article abstracts.
	The database is derived from the NCI PID Pathway Interaction Database, and the textual mentions are extracted from cooccurring pairs of genes in PubMed abstracts, processed and annotated by Literome. This dataset was used in the paper "Compositional Learning of Embeddings for Relation Paths in Knowledge Bases and Text".

	FILE FORMAT DETAILS

	The files train.txt, valid.txt, and test.text contain the training, development, and test set knowledge base (database of regulation relationships) triples used in.
	The file text.txt contains the textual triples derived from PubMed via entity linking and processing with Literome. The textual mentions were used for knowledge base completion in.

	The separator is a tab character; the relations are Positive_regulation, Negative_regulation, and Family (Family relationships occur only in the training set).

	The format is:

	\| GENE1 \| relation \| GENE2 \|

	Example:
	ABL1 Positive_regulation CDK2

	The separator is a tab character; the relations are Positive_regulation, Negative_regulation, and Family (Family relationships occur only in the training set).

	### Data Instances

	[More Information Needed]

	### Data Fields

	The format is:

	\| GENE1 \| relation \| GENE2 \|

	### Data Splits

	[More Information Needed]

	## Dataset Creation

	[More Information Needed]

	### Curation Rationale

	[More Information Needed]

	### Source Data

	[More Information Needed]

	#### Initial Data Collection and Normalization

	[More Information Needed]

	#### Who are the source language producers?

	[More Information Needed]

	### Annotations

	[More Information Needed]

	#### Annotation process

	[More Information Needed]

	#### Who are the annotators?

	[More Information Needed]

	### Personal and Sensitive Information

	[More Information Needed]

	## Considerations for Using the Data

	[More Information Needed]

	### Social Impact of Dataset

	[More Information Needed]

	### Discussion of Biases

	[More Information Needed]

	### Other Known Limitations

	[More Information Needed]

	## Additional Information

	[More Information Needed]

	### Dataset Curators

	The dataset was initially created by Kristina Toutanova, Victoria Lin, Wen-tau Yih, Hoifung Poon and Chris Quirk, during work done at Microsoft Research.

	### Licensing Information

	[More Information Needed]

	### Citation Information

	```
	@inproceedings{toutanova-etal-2016-compositional,
	title = "Compositional Learning of Embeddings for Relation Paths in Knowledge Base and Text",
	author = "Toutanova, Kristina and
	Lin, Victoria and
	Yih, Wen-tau and
	Poon, Hoifung and
	Quirk, Chris",
	booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
	month = aug,
	year = "2016",
	address = "Berlin, Germany",
	publisher = "Association for Computational Linguistics",
	url = "https://www.aclweb.org/anthology/P16-1136",
	doi = "10.18653/v1/P16-1136",
	pages = "1434--1444",
	}
	```

	### Contributions

	Thanks to [@manandey](https://github.com/manandey) for adding this dataset.