cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh3599166.08fu
title: SH3599166.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh3599167.08fu
title: SH3599167.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh3599168.08fu
title: SH3599168.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.5281/zenodo.6816433
title: CIViCmine

description: This describes the output files for the CIViCmine project. These files are loaded directly by the CIViCmine viewer. The code for this viewer is available in the CIViCmine Github repo if you want to run it independently. Each file is a tab-delimited file with a header, no comments and no quoting. You likely want <strong>civicmine_collated.tsv</strong> if you just want the list of cancer biomarkers. If you want the supporting sentences, look at <strong>civicmine_sentences.tsv</strong>. You can use the <em>matching_id</em> column to connect the two files. If you want to dig further and are okay with a higher false positive rate, look at <strong>civicmine_unfiltered.tsv</strong>. <strong>civicmine_collated.tsv:</strong> This contains the cancer biomarkers with citation counts supporting them. It contains the normalized cancer and gene names along with IDs for HUGO, Entrez Gene and the Disease Ontology. <strong>civicmine_sentences.tsv:</strong> This contains the supporting sentences for the cancer biomarker in the collated file. Each row is a single supporting sentence for one cancer biomarker. This file contains information on the source publication (e.g. journal, publication date, etc), the actual sentence and the cancer biomarker extracted. <strong>civicmine_unfiltered.tsv:</strong> This is the raw output of the applyModelsToSentences.py script across all of PubMed, Pubmed Central Open Access and PubMed Central Author Manuscript Collection. It contains every predicted relation with a prediction score above 0.5. So this may contain many false positives. Each row contain information on the publication (e.g. journal, publication date, etc) along with the sentence and the specific cancer biomarker extracted (with HUGO, Entrez Gene and Disease Ontology IDs). This file is further processed to create the other two.
DOI: 10.5281/zenodo.7103135
title: CIViCmine

description: This describes the output files for the CIViCmine project. These files are loaded directly by the CIViCmine viewer. The code for this viewer is available in the CIViCmine Github repo if you want to run it independently. Each file is a tab-delimited file with a header, no comments and no quoting. You likely want <strong>civicmine_collated.tsv</strong> if you just want the list of cancer biomarkers. If you want the supporting sentences, look at <strong>civicmine_sentences.tsv</strong>. You can use the <em>matching_id</em> column to connect the two files. If you want to dig further and are okay with a higher false positive rate, look at <strong>civicmine_unfiltered.tsv</strong>. <strong>civicmine_collated.tsv:</strong> This contains the cancer biomarkers with citation counts supporting them. It contains the normalized cancer and gene names along with IDs for HUGO, Entrez Gene and the Disease Ontology. <strong>civicmine_sentences.tsv:</strong> This contains the supporting sentences for the cancer biomarker in the collated file. Each row is a single supporting sentence for one cancer biomarker. This file contains information on the source publication (e.g. journal, publication date, etc), the actual sentence and the cancer biomarker extracted. <strong>civicmine_unfiltered.tsv:</strong> This is the raw output of the applyModelsToSentences.py script across all of PubMed, Pubmed Central Open Access and PubMed Central Author Manuscript Collection. It contains every predicted relation with a prediction score above 0.5. So this may contain many false positives. Each row contain information on the publication (e.g. journal, publication date, etc) along with the sentence and the specific cancer biomarker extracted (with HUGO, Entrez Gene and Disease Ontology IDs). This file is further processed to create the other two.
DOI: 10.5281/zenodo.7534491
title: CIViCmine

description: This describes the output files for the CIViCmine project. These files are loaded directly by the CIViCmine viewer. The code for this viewer is available in the CIViCmine Github repo if you want to run it independently. Each file is a tab-delimited file with a header, no comments and no quoting. You likely want <strong>civicmine_collated.tsv</strong> if you just want the list of cancer biomarkers. If you want the supporting sentences, look at <strong>civicmine_sentences.tsv</strong>. You can use the <em>matching_id</em> column to connect the two files. If you want to dig further and are okay with a higher false positive rate, look at <strong>civicmine_unfiltered.tsv</strong>. <strong>civicmine_collated.tsv:</strong> This contains the cancer biomarkers with citation counts supporting them. It contains the normalized cancer and gene names along with IDs for HUGO, Entrez Gene and the Disease Ontology. <strong>civicmine_sentences.tsv:</strong> This contains the supporting sentences for the cancer biomarker in the collated file. Each row is a single supporting sentence for one cancer biomarker. This file contains information on the source publication (e.g. journal, publication date, etc), the actual sentence and the cancer biomarker extracted. <strong>civicmine_unfiltered.tsv:</strong> This is the raw output of the applyModelsToSentences.py script across all of PubMed, Pubmed Central Open Access and PubMed Central Author Manuscript Collection. It contains every predicted relation with a prediction score above 0.5. So this may contain many false positives. Each row contain information on the publication (e.g. journal, publication date, etc) along with the sentence and the specific cancer biomarker extracted (with HUGO, Entrez Gene and Disease Ontology IDs). This file is further processed to create the other two.
DOI: 10.5281/zenodo.7689629
title: CIViCmine

description: This describes the output files for the CIViCmine project. These files are loaded directly by the CIViCmine viewer. The code for this viewer is available in the CIViCmine Github repo if you want to run it independently. Each file is a tab-delimited file with a header, no comments and no quoting. You likely want <strong>civicmine_collated.tsv</strong> if you just want the list of cancer biomarkers. If you want the supporting sentences, look at <strong>civicmine_sentences.tsv</strong>. You can use the <em>matching_id</em> column to connect the two files. If you want to dig further and are okay with a higher false positive rate, look at <strong>civicmine_unfiltered.tsv</strong>. <strong>civicmine_collated.tsv:</strong> This contains the cancer biomarkers with citation counts supporting them. It contains the normalized cancer and gene names along with IDs for HUGO, Entrez Gene and the Disease Ontology. <strong>civicmine_sentences.tsv:</strong> This contains the supporting sentences for the cancer biomarker in the collated file. Each row is a single supporting sentence for one cancer biomarker. This file contains information on the source publication (e.g. journal, publication date, etc), the actual sentence and the cancer biomarker extracted. <strong>civicmine_unfiltered.tsv:</strong> This is the raw output of the applyModelsToSentences.py script across all of PubMed, Pubmed Central Open Access and PubMed Central Author Manuscript Collection. It contains every predicted relation with a prediction score above 0.5. So this may contain many false positives. Each row contain information on the publication (e.g. journal, publication date, etc) along with the sentence and the specific cancer biomarker extracted (with HUGO, Entrez Gene and Disease Ontology IDs). This file is further processed to create the other two.
DOI: 10.5281/zenodo.1156242
title: Cancermine

description: Results from tool executed using PubRunner on MEDLINE corpus.
DOI: 10.5281/zenodo.1302062
title: Cancermine

description: This describes the output files for the CancerMine project. These files are loaded directly by the CancerMine viewer. The code for this viewer is available in the CancerMine Github repo if you want to run it independently. Each file is a tab-delimited file with a header, no comments and no quoting.

You likely want <strong>cancermine_collated.tsv</strong> if you just want the list of cancer gene roles. If you want the supporting sentences, look at <strong>cancermine_sentences.tsv</strong>. You can use the <em>matching_id</em> column to connect the two files. If you want to dig further and are okay with a higher false positive rate, look at <strong>cancermine_unfiltered.tsv</strong>.

<strong>cancermine_collated.tsv:</strong> This contains the cancer gene roles with citation counts supporting them. It contains the normalized cancer and gene names along with IDs for HUGO, Entrez Gene and the Disease Ontology.

<strong>cancermine_sentences.tsv:</strong> This contains the supporting sentences for the cancer gene roles in the collated file. Each row is a single supporting sentence for one cancer gene role. This file contains information on the source publication (e.g. journal, publication date, etc), the actual sentence and the cancer gene role extracted.

<strong>cancermine_unfiltered.tsv:</strong> This is the raw output of the applyModelsToSentences.py script across all of PubMed, Pubmed Central Open Access and PubMed Central Author Manuscript Collection. It contains every predicted relation with a prediction score above 0.5. So this may contain many false positives. Each row contain information on the publication (e.g. journal, publication date, etc) along with the sentence and the specific cancer gene role extracted (with HUGO, Entrez Gene and Disease Ontology IDs). This file is further processed to create the other two.
DOI: 10.5281/zenodo.1336650
title: Cancermine

description: This describes the output files for the CancerMine project. These files are loaded directly by the CancerMine viewer. The code for this viewer is available in the CancerMine Github repo if you want to run it independently. Each file is a tab-delimited file with a header, no comments and no quoting.

You likely want <strong>cancermine_collated.tsv</strong> if you just want the list of cancer gene roles. If you want the supporting sentences, look at <strong>cancermine_sentences.tsv</strong>. You can use the <em>matching_id</em> column to connect the two files. If you want to dig further and are okay with a higher false positive rate, look at <strong>cancermine_unfiltered.tsv</strong>.

<strong>cancermine_collated.tsv:</strong> This contains the cancer gene roles with citation counts supporting them. It contains the normalized cancer and gene names along with IDs for HUGO, Entrez Gene and the Disease Ontology.

<strong>cancermine_sentences.tsv:</strong> This contains the supporting sentences for the cancer gene roles in the collated file. Each row is a single supporting sentence for one cancer gene role. This file contains information on the source publication (e.g. journal, publication date, etc), the actual sentence and the cancer gene role extracted.

<strong>cancermine_unfiltered.tsv:</strong> This is the raw output of the applyModelsToSentences.py script across all of PubMed, Pubmed Central Open Access and PubMed Central Author Manuscript Collection. It contains every predicted relation with a prediction score above 0.5. So this may contain many false positives. Each row contain information on the publication (e.g. journal, publication date, etc) along with the sentence and the specific cancer gene role extracted (with HUGO, Entrez Gene and Disease Ontology IDs). This file is further processed to create the other two.
DOI: 10.5281/zenodo.1409552
title: Cancermine

description: This describes the output files for the CancerMine project. These files are loaded directly by the CancerMine viewer. The code for this viewer is available in the CancerMine Github repo if you want to run it independently. Each file is a tab-delimited file with a header, no comments and no quoting.

You likely want <strong>cancermine_collated.tsv</strong> if you just want the list of cancer gene roles. If you want the supporting sentences, look at <strong>cancermine_sentences.tsv</strong>. You can use the <em>matching_id</em> column to connect the two files. If you want to dig further and are okay with a higher false positive rate, look at <strong>cancermine_unfiltered.tsv</strong>.

<strong>cancermine_collated.tsv:</strong> This contains the cancer gene roles with citation counts supporting them. It contains the normalized cancer and gene names along with IDs for HUGO, Entrez Gene and the Disease Ontology.

<strong>cancermine_sentences.tsv:</strong> This contains the supporting sentences for the cancer gene roles in the collated file. Each row is a single supporting sentence for one cancer gene role. This file contains information on the source publication (e.g. journal, publication date, etc), the actual sentence and the cancer gene role extracted.

<strong>cancermine_unfiltered.tsv:</strong> This is the raw output of the applyModelsToSentences.py script across all of PubMed, Pubmed Central Open Access and PubMed Central Author Manuscript Collection. It contains every predicted relation with a prediction score above 0.5. So this may contain many false positives. Each row contain information on the publication (e.g. journal, publication date, etc) along with the sentence and the specific cancer gene role extracted (with HUGO, Entrez Gene and Disease Ontology IDs). This file is further processed to create the other two.
DOI: 10.5281/zenodo.1457682
title: Cancermine

description: This describes the output files for the CancerMine project. These files are loaded directly by the CancerMine viewer. The code for this viewer is available in the CancerMine Github repo if you want to run it independently. Each file is a tab-delimited file with a header, no comments and no quoting.

You likely want <strong>cancermine_collated.tsv</strong> if you just want the list of cancer gene roles. If you want the supporting sentences, look at <strong>cancermine_sentences.tsv</strong>. You can use the <em>matching_id</em> column to connect the two files. If you want to dig further and are okay with a higher false positive rate, look at <strong>cancermine_unfiltered.tsv</strong>.

<strong>cancermine_collated.tsv:</strong> This contains the cancer gene roles with citation counts supporting them. It contains the normalized cancer and gene names along with IDs for HUGO, Entrez Gene and the Disease Ontology.

<strong>cancermine_sentences.tsv:</strong> This contains the supporting sentences for the cancer gene roles in the collated file. Each row is a single supporting sentence for one cancer gene role. This file contains information on the source publication (e.g. journal, publication date, etc), the actual sentence and the cancer gene role extracted.

<strong>cancermine_unfiltered.tsv:</strong> This is the raw output of the applyModelsToSentences.py script across all of PubMed, Pubmed Central Open Access and PubMed Central Author Manuscript Collection. It contains every predicted relation with a prediction score above 0.5. So this may contain many false positives. Each row contain information on the publication (e.g. journal, publication date, etc) along with the sentence and the specific cancer gene role extracted (with HUGO, Entrez Gene and Disease Ontology IDs). This file is further processed to create the other two.
DOI: 10.5281/zenodo.1481254
title: Cancermine

description: This describes the output files for the CancerMine project. These files are loaded directly by the CancerMine viewer. The code for this viewer is available in the CancerMine Github repo if you want to run it independently. Each file is a tab-delimited file with a header, no comments and no quoting.

You likely want <strong>cancermine_collated.tsv</strong> if you just want the list of cancer gene roles. If you want the supporting sentences, look at <strong>cancermine_sentences.tsv</strong>. You can use the <em>matching_id</em> column to connect the two files. If you want to dig further and are okay with a higher false positive rate, look at <strong>cancermine_unfiltered.tsv</strong>.

<strong>cancermine_collated.tsv:</strong> This contains the cancer gene roles with citation counts supporting them. It contains the normalized cancer and gene names along with IDs for HUGO, Entrez Gene and the Disease Ontology.

<strong>cancermine_sentences.tsv:</strong> This contains the supporting sentences for the cancer gene roles in the collated file. Each row is a single supporting sentence for one cancer gene role. This file contains information on the source publication (e.g. journal, publication date, etc), the actual sentence and the cancer gene role extracted.

<strong>cancermine_unfiltered.tsv:</strong> This is the raw output of the applyModelsToSentences.py script across all of PubMed, Pubmed Central Open Access and PubMed Central Author Manuscript Collection. It contains every predicted relation with a prediction score above 0.5. So this may contain many false positives. Each row contain information on the publication (e.g. journal, publication date, etc) along with the sentence and the specific cancer gene role extracted (with HUGO, Entrez Gene and Disease Ontology IDs). This file is further processed to create the other two.
DOI: 10.5281/zenodo.1887736
title: CancerMine

description: This describes the output files for the CancerMine project. These files are loaded directly by the CancerMine viewer. The code for this viewer is available in the CancerMine Github repo if you want to run it independently. Each file is a tab-delimited file with a header, no comments and no quoting.

You likely want <strong>cancermine_collated.tsv</strong> if you just want the list of cancer gene roles. If you want the supporting sentences, look at <strong>cancermine_sentences.tsv</strong>. You can use the <em>matching_id</em> column to connect the two files. If you want to dig further and are okay with a higher false positive rate, look at <strong>cancermine_unfiltered.tsv</strong>.

<strong>cancermine_collated.tsv:</strong> This contains the cancer gene roles with citation counts supporting them. It contains the normalized cancer and gene names along with IDs for HUGO, Entrez Gene and the Disease Ontology.

<strong>cancermine_sentences.tsv:</strong> This contains the supporting sentences for the cancer gene roles in the collated file. Each row is a single supporting sentence for one cancer gene role. This file contains information on the source publication (e.g. journal, publication date, etc), the actual sentence and the cancer gene role extracted.

<strong>cancermine_unfiltered.tsv:</strong> This is the raw output of the applyModelsToSentences.py script across all of PubMed, Pubmed Central Open Access and PubMed Central Author Manuscript Collection. It contains every predicted relation with a prediction score above 0.5. So this may contain many false positives. Each row contain information on the publication (e.g. journal, publication date, etc) along with the sentence and the specific cancer gene role extracted (with HUGO, Entrez Gene and Disease Ontology IDs). This file is further processed to create the other two.