DISEASES 2.0: a weekly updated database of disease–gene associations from text mining and data integration

The scientific knowledge about which genes are involved in which diseases grows rapidly, which makes it difficult to keep up with new publications and genetics datasets. The DISEASES database aims to provide a comprehensive overview by systematically integrating and assigning confidence scores to evidence for disease–gene associations from curated databases, genome-wide association studies (GWAS), and automatic text mining of the biomedical literature. Here, we present a major update to this resource, which greatly increases the number of associations from all these sources. This is especially true for the text-mined associations, which have increased by at least 9-fold at all confidence cutoffs. We show that this dramatic increase is primarily due to adding full-text articles to the text corpus, secondarily due to improvements to both the disease and gene dictionaries used for named entity recognition, and only to a very small extent due to the growth in number of PubMed abstracts. DISEASES now also makes use of a new GWAS database, TIGA, which considerably increased the number of GWAS-derived disease–gene associations. DISEASES itself is also integrated into several other databases and resources, including GeneCards/MalaCards, Pharos/TCRD, and the Cytoscape stringApp. All data in DISEASES is updated on a weekly basis and is available via a web interface at https://diseases.jensenlab.org, from where it can also be downloaded under open licenses.

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DISEASES: Text mining and data integration of disease–gene associations

10.1101/008425 ◽

2014 ◽

Cited By ~ 7

Author(s):

Sune Pletscher-Frankild ◽

Albert Pallejà ◽

Kalliopi Tsafou ◽

Janos X Binder ◽

Lars Juhl Jensen

Keyword(s):

Text Mining ◽

Disease Gene ◽

Association Studies ◽

False Positive Rate ◽

Named Entity Recognition ◽

Entity Recognition ◽

Genome Wide Association Studies ◽

Human Genes ◽

Gene Associations ◽

Positive Rate

Text mining is a flexible technology that can be applied to numerous different tasks in biology and medicine. We present a system for extracting disease–gene associations from biomedical abstracts. The system consists of a highly efficient dictionary-based tagger for named entity recognition of human genes and diseases, which we combine with a scoring scheme that takes into account co-occurrences both within and between sentences. We show that this approach is able to extract half of all manually curated associations with a false positive rate of only 0.16%. Nonetheless, text mining should not stand alone, but be combined with other types of evidence. For this reason, we have developed the DISEASES resource, which integrates the results from text mining with manually curated disease–gene associations, cancer mutation data, and genome-wide association studies from existing databases. The DISEASES resource is accessible through a user-friendly web interface at http://diseases.jensenlab.org/, where the text-mining software and all associations are also freely available for download.

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Epistasis analysis reveals associations between gene variants and bipolar disorder

10.7287/peerj.preprints.3242v1 ◽

2017 ◽

Author(s):

Carlo Maj ◽

Elena Milanesi ◽

Massimo Gennarelli ◽

Luciano Milanesi ◽

ivan Merelli

Keyword(s):

Bipolar Disorder ◽

Association Studies ◽

Statistical Tests ◽

Genome Wide Association Studies ◽

Epistasis Analysis ◽

Complex Phenotypes ◽

Genome Wide ◽

A Genome ◽

Gene Associations ◽

Wide Scale

In complex phenotypes (e.g., psychiatric diseases) single locus tests, commonly performed with Genome-Wide Association Studies, have proven to be limited in discovering strong gene associations. A growing body of evidence suggests that epistatic non-linear effects may be responsible for complex phenotypes arising from the interaction of different biological factors. A major issue in epistasis analysis is the computational burden due to the huge number of statistical tests to be performed when considering all the potential genotype combinations. In this work, we developed a computational efficient pipeline to investigate the presence of epistasis at a genome-wide scale in bipolar disorder, which is a typical example of complex phenotype with a relevant but unexplained genetic background. By running our pipeline we were able to identify 13 epistasis interactions between variants located in genes potentially involved in biological processes associated with the analyzed phenotype.

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Mining microbe–disease interactions from literature via a transfer learning model

BMC Bioinformatics ◽

10.1186/s12859-021-04346-7 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Chengkun Wu ◽

Xinyi Xiao ◽

Canqun Yang ◽

JinXiang Chen ◽

Jiacai Yi ◽

...

Keyword(s):

Text Mining ◽

Large Scale ◽

Named Entity Recognition ◽

Learning Model ◽

Biomedical Literature ◽

Fine Tuning ◽

Entity Recognition ◽

Interaction Extraction ◽

Biomedical Texts ◽

Data Browsing

Abstract Background Interactions of microbes and diseases are of great importance for biomedical research. However, large-scale of microbe–disease interactions are hidden in the biomedical literature. The structured databases for microbe–disease interactions are in limited amounts. In this paper, we aim to construct a large-scale database for microbe–disease interactions automatically. We attained this goal via applying text mining methods based on a deep learning model with a moderate curation cost. We also built a user-friendly web interface that allows researchers to navigate and query required information. Results Firstly, we manually constructed a golden-standard corpus and a sliver-standard corpus (SSC) for microbe–disease interactions for curation. Moreover, we proposed a text mining framework for microbe–disease interaction extraction based on a pretrained model BERE. We applied named entity recognition tools to detect microbe and disease mentions from the free biomedical texts. After that, we fine-tuned the pretrained model BERE to recognize relations between targeted entities, which was originally built for drug–target interactions or drug–drug interactions. The introduction of SSC for model fine-tuning greatly improved detection performance for microbe–disease interactions, with an average reduction in error of approximately 10%. The MDIDB website offers data browsing, custom searching for specific diseases or microbes, and batch downloading. Conclusions Evaluation results demonstrate that our method outperform the baseline model (rule-based PKDE4J) with an average $$F_1$$ F 1 -score of 73.81%. For further validation, we randomly sampled nearly 1000 predicted interactions by our model, and manually checked the correctness of each interaction, which gives a 73% accuracy. The MDIDB webiste is freely avaliable throuth http://dbmdi.com/index/

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A Knowledge-Driven Approach to Extract Disease-Related Biomarkers from the Literature

BioMed Research International ◽

10.1155/2014/253128 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11 ◽

Cited By ~ 31

Author(s):

À. Bravo ◽

M. Cases ◽

N. Queralt-Rosinach ◽

F. Sanz ◽

L. I. Furlong

Keyword(s):

Text Mining ◽

Named Entity Recognition ◽

Relation Extraction ◽

Recognition System ◽

Biomedical Literature ◽

Entity Recognition ◽

Scientific Publications ◽

Positive Ratio ◽

Related Information ◽

Mesh Terms

The biomedical literature represents a rich source of biomarker information. However, both the size of literature databases and their lack of standardization hamper the automatic exploitation of the information contained in these resources. Text mining approaches have proven to be useful for the exploitation of information contained in the scientific publications. Here, we show that a knowledge-driven text mining approach can exploit a large literature database to extract a dataset of biomarkers related to diseases covering all therapeutic areas. Our methodology takes advantage of the annotation of MEDLINE publications pertaining to biomarkers with MeSH terms, narrowing the search to specific publications and, therefore, minimizing the false positive ratio. It is based on a dictionary-based named entity recognition system and a relation extraction module. The application of this methodology resulted in the identification of 131,012 disease-biomarker associations between 2,803 genes and 2,751 diseases, and represents a valuable knowledge base for those interested in disease-related biomarkers. Additionally, we present a bibliometric analysis of the journals reporting biomarker related information during the last 40 years.

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Text mining of 15 million full-text scientific articles

10.1101/162099 ◽

2017 ◽

Cited By ~ 5

Author(s):

David Westergaard ◽

Hans-Henrik Stærfeldt ◽

Christian Tønsberg ◽

Lars Juhl Jensen ◽

Søren Brunak

Keyword(s):

Text Mining ◽

Full Text ◽

Disease Gene ◽

Scientific Literature ◽

Named Entity Recognition ◽

Recognition System ◽

Entity Recognition ◽

Data Sets ◽

Named Entity ◽

Benchmark Data

AbstractAcross academia and industry, text mining has become a popular strategy for keeping up with the rapid growth of the scientific literature. Text mining of the scientific literature has mostly been carried out on collections of abstracts, due to their availability. Here we present an analysis of 15 million English scientific full-text articles published during the period 1823–2016. We describe the development in article length and publication sub-topics during these nearly 250 years. We showcase the potential of text mining by extracting published protein–protein, disease–gene, and protein subcellular associations using a named entity recognition system, and quantitatively report on their accuracy using gold standard benchmark data sets. We subsequently compare the findings to corresponding results obtained on 16.5 million abstracts included in MEDLINE and show that text mining of full-text articles consistently outperforms using abstracts only.

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Integration of high-resolution promoter profiling assays reveals novel, cell type-specific transcription start sites across 115 human cell and tissue types

Genome Research ◽

10.1101/gr.275723.121 ◽

2021 ◽

pp. gr.275723.121

Author(s):

Jill E Moore ◽

Xiao-Ou Zhang ◽

Shaimae I Elhajjajy ◽

Kaili Fan ◽

Henry E Pratt ◽

...

Keyword(s):

Disease Gene ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Cell Type ◽

Transcription Start ◽

Transcription Start Sites ◽

Genome Wide ◽

Cell Type Specific ◽

Gwas Catalog

Accurate transcription start site (TSS) annotations are essential for understanding transcriptional regulation and its role in human disease. Gene collections such as GENCODE contain annotations for tens of thousands of TSSs, but not all of these annotations are experimentally validated, nor do they contain information on cell type-specific usage. Therefore, we sought to generate a collection of experimentally validated TSSs by integrating RNA Annotation and Mapping of Promoters for the Analysis of Gene Expression (RAMPAGE) data from 115 cell and tissue types, which resulted in a collection of approximately 50 thousand representative RAMPAGE peaks. These peaks were primarily proximal to GENCODE-annotated TSSs and were concordant with other transcription assays. Because RAMPAGE uses paired-end reads, we were then able to connect peaks to transcripts by analyzing the genomic positions of the 3' ends of read mates. Using this paired-end information, we classified the vast majority (37 thousand) of our RAMPAGE peaks as verified TSSs, updating TSS annotations for 20% of GENCODE genes. We also found that these updated TSS annotations were supported by epigenomic and other transcriptomic datasets. To demonstrate the utility of this RAMPAGE rPeak collection, we intersected it with the NHGRI/EBI genome-wide association studies (GWAS) catalog and identified new candidate GWAS genes. Overall, our work demonstrates the importance of integrating experimental data to further refine TSS annotations and provides a valuable resource for the biological community.

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Epistasis analysis reveals associations between gene variants and bipolar disorder

10.7287/peerj.preprints.3242 ◽

2017 ◽

Author(s):

Carlo Maj ◽

Elena Milanesi ◽

Massimo Gennarelli ◽

Luciano Milanesi ◽

ivan Merelli

Keyword(s):

Bipolar Disorder ◽

Association Studies ◽

Statistical Tests ◽

Genome Wide Association Studies ◽

Epistasis Analysis ◽

Complex Phenotypes ◽

Genome Wide ◽

A Genome ◽

Gene Associations ◽

Wide Scale

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TRIBES: A user-friendly pipeline for relatedness detection and disease gene discovery

10.1101/686253 ◽

2019 ◽

Cited By ~ 2

Author(s):

Natalie A. Twine ◽

Piotr Szul ◽

Lyndal Henden ◽

Emily P. McCann ◽

Ian P. Blair ◽

...

Keyword(s):

Disease Gene ◽

Association Studies ◽

Supplementary Information ◽

Genome Wide Association Studies ◽

Susceptibility Loci ◽

Disease Gene Discovery ◽

Genome Wide ◽

Single User ◽

Relationship Structures ◽

User Friendly

AbstractSummaryTRIBES is a user-friendly pipeline for relatedness detection in genomic data. TRIBES is the first tool which is both accurate up to 7th degree relatives (e.g. third cousins) and combines essential data processing steps into a single user-friendly pipeline. Furthermore, using a proof-of-principle cohort comprising amyotrophic lateral sclerosis cases with known relationship structures and a known causal mutation in SOD1, we demonstrated that TRIBES can successfully uncover disease susceptibility loci. TRIBES has multiple applications in addition to disease gene mapping, including sample quality control in genome wide association studies and avoiding consanguineous unions in family planning.AvailabilityTRIBES is freely available on GitHub: https://github.com/aehrc/TRIBES/[email protected] informationXXXX

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