Datasets and Software

Stopwords for biomedical literature: [DOWNLOAD]

PubMed Phrases: [DOWNLOAD]

PubMed Phrases are coherent text segments that are beneficial for information retrieval and human comprehension.

PubMed Phrases, an open set of coherent phrases for searching biomedical literature, S. Kim, L. Yeganova, D. C. Comeau, W. J. Wilbur and Z. Lu, Scientific Data, 5, 180104, 2018.

Word embeddings for PubMed: [DOWNLOAD]

Word vectors (in word2vec binary format) trained on all PubMed abstracts (Mar. 2016).

Bridging the gap: incorporating a semantic similarity measure for effectively mapping PubMed queries to documents, S. Kim, N. Fiorini, W. J. Wilbur and Z. Lu, Journal of Biomedical Informatics, 75, pp. 122-127, 2017 (original version in arXiv).

NCBITextLib: [LINK]

Software library for building a large-scale data infrastructure for text mining.

Meshable dataset (MeSH terms): [DOWNLOAD]
Meshable dataset (MeSH term/subheading combinations): [DOWNLOAD]

MeSH terms (sorted by document set size) and their top 100 topic terms with alpha scores. The PubMed set used to obtain topic terms was collected in Oct. 2015.

Meshable: searching PubMed abstracts by utilizing MeSH and MeSH-derived topical terms, S. Kim, L. Yeganova and W. J. Wilbur, Bioinformatics, 32(19), pp. 3044-3046, 2016.

Cystic fibrosis, Deafness, DiGeorge syndrome, Autism and Hypertrophic cardiomyopathy datasets: [LINK]

PubMed IDs used for evaluating the PAV-EM thematic clustering algorithm.

Summarizing topical contents from PubMed documents using a thematic analysis, S. Kim, L. Yeganova, and W. J. Wilbur, Conference on Empirical Methods on Natural Language Processing (EMNLP), pp. 805-810, 2015.

Feature generation tool for DDIExtraction corpora: [LINK]

The tool creates a feature set for the DDIExtraction 2013 corpus.

Extracting drug-drug interactions from literature using a rich feature-based linear kernel approach, S. Kim, H. Liu, L. Yeganova, and W. J. Wilbur, Journal of Biomedical Informatics, 55, pp. 23-30, 2015.

Disease, CellLine and Reptiles datasets: [DOWNLOAD]

Gene names annotated for PubMed documents.

Classifying gene sentences in biomedical literature by combining high-precision gene identifiers, S. Kim, W. Kim, D. Comeau, and W. J. Wilbur, NAACL 2012 Workshop on Biomedical Natural Language Processing (BioNLP), pp. 185-192, 2012.

This work is a United States Government Work under the terms of the United States Copyright Act. It was written as part of the authors' official duties as a United States Government employee and thus cannot be copyrighted within the United States. The data is freely available to the public for use. The National Library of Medicine and the U.S. Government have not placed any restriction on its use or reproduction.

Although all reasonable efforts have been taken to ensure the accuracy and reliability of the data and its source code, the NLM and the U.S. Government do not and cannot warrant the performance or results that may be obtained by using it. The NLM and the U.S. Government disclaim all warranties, express or implied, including warranties of performance, merchantability or fitness for any particular purpose.