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Article ; Online: Analyzing the vast coronavirus literature with CoronaCentral.

Lever, Jake / Altman, Russ B

Proceedings of the National Academy of Sciences of the United States of America

2021 Volume 118, Issue 23

Abstract: The SARS-CoV-2 pandemic has caused a surge in research exploring all aspects of the virus and its effects on human health. The overwhelming publication rate means that researchers are unable to keep abreast of the literature. To ameliorate this, we ... ...

Abstract	The SARS-CoV-2 pandemic has caused a surge in research exploring all aspects of the virus and its effects on human health. The overwhelming publication rate means that researchers are unable to keep abreast of the literature. To ameliorate this, we present the CoronaCentral resource that uses machine learning to process the research literature on SARS-CoV-2 together with SARS-CoV and MERS-CoV. We categorize the literature into useful topics and article types and enable analysis of the contents, pace, and emphasis of research during the crisis with integration of Altmetric data. These topics include therapeutics, disease forecasting, as well as growing areas such as "long COVID" and studies of inequality. This resource, available at https://coronacentral.ai, is updated daily.
MeSH term(s)	Animals ; COVID-19/epidemiology ; COVID-19/metabolism ; COVID-19/therapy ; COVID-19/transmission ; Humans ; Machine Learning ; Middle East Respiratory Syndrome Coronavirus/metabolism ; Middle East Respiratory Syndrome Coronavirus/pathogenicity ; Pandemics ; SARS-CoV-2/metabolism ; SARS-CoV-2/pathogenicity ; Severe Acute Respiratory Syndrome/epidemiology ; Severe Acute Respiratory Syndrome/metabolism ; Severe Acute Respiratory Syndrome/therapy ; Severe Acute Respiratory Syndrome/transmission
Language	English
Publishing date	2021-04-14
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Review
ZDB-ID	209104-5
ISSN	1091-6490 ; 0027-8424
ISSN (online)	1091-6490
ISSN	0027-8424
DOI	10.1073/pnas.2100766118
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Analyzing the vast coronavirus literature with CoronaCentral.

Lever, Jake / Altman, Russ B

bioRxiv : the preprint server for biology

2020

Abstract: The global SARS-CoV-2 pandemic has caused a surge in research exploring all aspects of the virus and its effects on human health. The overwhelming rate of publications means that human researchers are unable to keep abreast of the research. To ameliorate ...

Abstract	The global SARS-CoV-2 pandemic has caused a surge in research exploring all aspects of the virus and its effects on human health. The overwhelming rate of publications means that human researchers are unable to keep abreast of the research. To ameliorate this, we present the CoronaCentral resource which uses machine learning to process the research literature on SARS-CoV-2 along with articles on SARS-CoV and MERS-CoV. We break the literature down into useful categories and enable analysis of the contents, pace, and emphasis of research during the crisis. These categories cover therapeutics, forecasting as well as growing areas such as "Long Covid" and studies of inequality and misinformation. Using this data, we compare topics that appear in original research articles compared to commentaries and other article types. Finally, using Altmetric data, we identify the topics that have gained the most media attention. This resource, available at https://coronacentral.ai , is updated multiple times per day and provides an easy-to-navigate system to find papers in different categories, focussing on different aspects of the virus along with currently trending articles.
Language	English
Publishing date	2020-12-22
Publishing country	United States
Document type	Preprint
DOI	10.1101/2020.12.21.423860
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Extending TextAE for annotation of non-contiguous entities.

Lever, Jake / Altman, Russ / Kim, Jin-Dong

Genomics & informatics

2020 Volume 18, Issue 2, Page(s) e15

Abstract: Named entity recognition tools are used to identify mentions of biomedical entities in free text and are essential components of high-quality information retrieval and extraction systems. Without good entity recognition, methods will mislabel searched ... ...

Abstract	Named entity recognition tools are used to identify mentions of biomedical entities in free text and are essential components of high-quality information retrieval and extraction systems. Without good entity recognition, methods will mislabel searched text and will miss important information or identify spurious text that will frustrate users. Most tools do not capture non-contiguous entities which are separate spans of text that together refer to an entity, e.g., the entity "type 1 diabetes" in the phrase "type 1 and type 2 diabetes." This type is commonly found in biomedical texts, especially in lists, where multiple biomedical entities are named in shortened form to avoid repeating words. Most text annotation systems, that enable users to view and edit entity annotations, do not support non-contiguous entities. Therefore, experts cannot even visualize non-contiguous entities, let alone annotate them to build valuable datasets for machine learning methods. To combat this problem and as part of the BLAH6 hackathon, we extended the TextAE platform to allow visualization and annotation of non-contiguous entities. This enables users to add new subspans to existing entities by selecting additional text. We integrate this new functionality with TextAE's existing editing functionality to allow easy changes to entity annotation and editing of relation annotations involving non-contiguous entities, with importing and exporting to the PubAnnotation format. Finally, we roughly quantify the problem across the entire accessible biomedical literature to highlight that there are a substantial number of non-contiguous entities that appear in lists that would be missed by most text mining systems.
Language	English
Publishing date	2020-06-15
Publishing country	Korea (South)
Document type	Journal Article
ZDB-ID	2802682-2
ISSN	2234-0742 ; 1598-866X
ISSN (online)	2234-0742
ISSN	1598-866X
DOI	10.5808/GI.2020.18.2.e15
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Analyzing the vast coronavirus literature with CoronaCentral

Lever, Jake / Altman, Russ B

bioRxiv

Abstract	The global SARS-CoV-2 pandemic has caused a surge in research exploring all aspects of the virus and its effects on human health. The overwhelming rate of publications means that human researchers are unable to keep abreast of the research. To ameliorate this, we present the CoronaCentral resource which uses machine learning to process the research literature on SARS-CoV-2 along with articles on SARS-CoV and MERS-CoV. We break the literature down into useful categories and enable analysis of the contents, pace, and emphasis of research during the crisis. These categories cover therapeutics, forecasting as well as growing areas such as "Long Covid" and studies of inequality and misinformation. Using this data, we compare topics that appear in original research articles compared to commentaries and other article types. Finally, using Altmetric data, we identify the topics that have gained the most media attention. This resource, available at https://coronacentral.ai, is updated multiple times per day and provides an easy-to-navigate system to find papers in different categories, focussing on different aspects of the virus along with currently trending articles.
Keywords	covid19
Language	English
Publishing date	2020-12-22
Publisher	Cold Spring Harbor Laboratory
Document type	Article ; Online
DOI	10.1101/2020.12.21.423860
Database	COVID19

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Article ; Online: Associating biological context with protein-protein interactions through text mining at PubMed scale.

Sosa, Daniel N / Hintzen, Rogier / Xiong, Betty / de Giorgio, Alex / Fauqueur, Julien / Davies, Mark / Lever, Jake / Altman, Russ B

Journal of biomedical informatics

2023 Volume 145, Page(s) 104474

Abstract: Inferring knowledge from known relationships between drugs, proteins, genes, and diseases has great potential for clinical impact, such as predicting which existing drugs could be repurposed to treat rare diseases. Incorporating key biological context ... ...

Abstract	Inferring knowledge from known relationships between drugs, proteins, genes, and diseases has great potential for clinical impact, such as predicting which existing drugs could be repurposed to treat rare diseases. Incorporating key biological context such as cell type or tissue of action into representations of extracted biomedical knowledge is essential for principled pharmacological discovery. Existing global, literature-derived knowledge graphs of interactions between drugs, proteins, genes, and diseases lack this essential information. In this study, we frame the task of associating biological context with protein-protein interactions extracted from text as a classification task using syntactic, semantic, and novel meta-discourse features. We introduce the Insider corpora, which are automatically generated PubMed-scale corpora for training classifiers for the context association task. These corpora are created by searching for precise syntactic cues of cell type and tissue relevancy to extracted regulatory relations. We report F1 scores of 0.955 and 0.862 for identifying relevant cell types and tissues, respectively, for our identified relations. By classifying with this framework, we demonstrate that the problem of context association can be addressed using intuitive, interpretable features. We demonstrate the potential of this approach to enrich text-derived knowledge bases with biological detail by incorporating cell type context into a protein-protein network for dengue fever.
MeSH term(s)	Humans ; PubMed ; Data Mining ; Knowledge Bases ; Rare Diseases
Language	English
Publishing date	2023-08-10
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2057141-0
ISSN	1532-0480 ; 1532-0464
ISSN (online)	1532-0480
ISSN	1532-0464
DOI	10.1016/j.jbi.2023.104474
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Repurposing biomedical informaticians for COVID-19.

Sosa, Daniel N / Chen, Binbin / Kaushal, Amit / Lavertu, Adam / Lever, Jake / Rensi, Stefano / Altman, Russ

Journal of biomedical informatics

2021 Volume 115, Page(s) 103673

Abstract: The COVID-19 pandemic is an unprecedented challenge to the biomedical research community at the intersection of great uncertainty due to the novelty of the virus and extremely high stakes due to the large global death count. The global quarantine shut- ... ...

Abstract	The COVID-19 pandemic is an unprecedented challenge to the biomedical research community at the intersection of great uncertainty due to the novelty of the virus and extremely high stakes due to the large global death count. The global quarantine shut-downs complicated scientific matters because many laboratories were closed down unless they were actively doing COVID-19 related research, making repurposing of activities difficult for many biomedical researchers. Biomedical informaticians, who have been primarily able to continue their research through remote work and video conferencing, have been able to maintain normal activities. In addition to continuing ongoing studies, there has been great grass roots interest in helping in the fight against COVID-19. In this commentary, we describe several projects that arose from this desire to help, and the lessons that the authors learned along the way. We then offer some insights into how these lessons might be applied to make scientific progress be more efficient in future crisis scenarios.
MeSH term(s)	Biomedical Research ; COVID-19/epidemiology ; COVID-19/virology ; Humans ; Medical Informatics ; SARS-CoV-2/isolation & purification
Language	English
Publishing date	2021-01-21
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2057141-0
ISSN	1532-0480 ; 1532-0464
ISSN (online)	1532-0480
ISSN	1532-0464
DOI	10.1016/j.jbi.2021.103673
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: LIONS: analysis suite for detecting and quantifying transposable element initiated transcription from RNA-seq.

Babaian, Artem / Thompson, I Richard / Lever, Jake / Gagnier, Liane / Karimi, Mohammad M / Mager, Dixie L

Bioinformatics (Oxford, England)

2019 Volume 35, Issue 19, Page(s) 3839–3841

Abstract: Summary: Transposable elements (TEs) influence the evolution of novel transcriptional networks yet the specific and meaningful interpretation of how TE-derived transcriptional initiation contributes to the transcriptome has been marred by computational ... ...

Abstract	Summary: Transposable elements (TEs) influence the evolution of novel transcriptional networks yet the specific and meaningful interpretation of how TE-derived transcriptional initiation contributes to the transcriptome has been marred by computational and methodological deficiencies. We developed LIONS for the analysis of RNA-seq data to specifically detect and quantify TE-initiated transcripts. Availability and implementation: Source code, container, test data and instruction manual are freely available at www.github.com/ababaian/LIONS. Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	DNA Transposable Elements ; RNA-Seq ; Software ; Whole Exome Sequencing
Chemical Substances	DNA Transposable Elements
Language	English
Publishing date	2019-02-21
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btz130
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: CancerMine: a literature-mined resource for drivers, oncogenes and tumor suppressors in cancer.

Lever, Jake / Zhao, Eric Y / Grewal, Jasleen / Jones, Martin R / Jones, Steven J M

Nature methods

2019 Volume 16, Issue 6, Page(s) 505–507

Abstract: Tumors from individuals with cancer are frequently genetically profiled for information about the driving forces behind the disease. We present the CancerMine resource, a text-mined and routinely updated database of drivers, oncogenes and tumor ... ...

Abstract	Tumors from individuals with cancer are frequently genetically profiled for information about the driving forces behind the disease. We present the CancerMine resource, a text-mined and routinely updated database of drivers, oncogenes and tumor suppressors in different types of cancer. All data are available online ( http://bionlp.bcgsc.ca/cancermine ) and downloadable under a Creative Commons Zero license for ease of use.
MeSH term(s)	Data Mining/methods ; Databases, Factual ; Gene Expression Regulation, Neoplastic ; Genes, Tumor Suppressor ; Genome, Human ; Humans ; Neoplasms/genetics ; Oncogenes ; Periodicals as Topic ; Software
Language	English
Publishing date	2019-05-20
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2169522-2
ISSN	1548-7105 ; 1548-7091
ISSN (online)	1548-7105
ISSN	1548-7091
DOI	10.1038/s41592-019-0422-y
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Search and visualization of gene-drug-disease interactions for pharmacogenomics and precision medicine research using GeneDive.

Wong, Mike / Previde, Paul / Cole, Jack / Thomas, Brook / Laxmeshwar, Nayana / Mallory, Emily / Lever, Jake / Petkovic, Dragutin / Altman, Russ B / Kulkarni, Anagha

Journal of biomedical informatics

2021 Volume 117, Page(s) 103732

Abstract: Background: Understanding the relationships between genes, drugs, and disease states is at the core of pharmacogenomics. Two leading approaches for identifying these relationships in medical literature are: human expert led manual curation efforts, and ... ...

Abstract	Background: Understanding the relationships between genes, drugs, and disease states is at the core of pharmacogenomics. Two leading approaches for identifying these relationships in medical literature are: human expert led manual curation efforts, and modern data mining based automated approaches. The former generates small amounts of high-quality data, and the latter offers large volumes of mixed quality data. The algorithmically extracted relationships are often accompanied by supporting evidence, such as, confidence scores, source articles, and surrounding contexts (excerpts) from the articles, that can be used as data quality indicators. Tools that can leverage these quality indicators to help the user gain access to larger and high-quality data are needed. Approach: We introduce GeneDive, a web application for pharmacogenomics researchers and precision medicine practitioners that makes gene, disease, and drug interactions data easily accessible and usable. GeneDive is designed to meet three key objectives: (1) provide functionality to manage information-overload problem and facilitate easy assimilation of supporting evidence, (2) support longitudinal and exploratory research investigations, and (3) offer integration of user-provided interactions data without requiring data sharing. Results: GeneDive offers multiple search modalities, visualizations, and other features that guide the user efficiently to the information of their interest. To facilitate exploratory research, GeneDive makes the supporting evidence and context for each interaction readily available and allows the data quality threshold to be controlled by the user as per their risk tolerance level. The interactive search-visualization loop enables relationship discoveries between diseases, genes, and drugs that might not be explicitly described in literature but are emergent from the source medical corpus and deductive reasoning. The ability to utilize user's data either in combination with the GeneDive native datasets or in isolation promotes richer data-driven exploration and discovery. These functionalities along with GeneDive's applicability for precision medicine, bringing the knowledge contained in biomedical literature to bear on particular clinical situations and improving patient care, are illustrated through detailed use cases. Conclusion: GeneDive is a comprehensive, broad-use biological interactions browser. The GeneDive application and information about its underlying system architecture are available at http://www.genedive.net. GeneDive Docker image is also available for download at this URL, allowing users to (1) import their own interaction data securely and privately; and (2) generate and test hypotheses across their own and other datasets.
MeSH term(s)	Data Mining ; Humans ; Pharmaceutical Preparations ; Pharmacogenetics ; Precision Medicine ; Software
Chemical Substances	Pharmaceutical Preparations
Language	English
Publishing date	2021-03-16
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2057141-0
ISSN	1532-0480 ; 1532-0464
ISSN (online)	1532-0480
ISSN	1532-0464
DOI	10.1016/j.jbi.2021.103732
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: PubRunner: A light-weight framework for updating text mining results.

Anekalla, Kishore R / Courneya, J P / Fiorini, Nicolas / Lever, Jake / Muchow, Michael / Busby, Ben

F1000Research

2017 Volume 6, Page(s) 612

Abstract: Biomedical text mining promises to assist biologists in quickly navigating the combined knowledge in their domain. This would allow improved understanding of the complex interactions within biological systems and faster hypothesis generation. New ... ...

Abstract	Biomedical text mining promises to assist biologists in quickly navigating the combined knowledge in their domain. This would allow improved understanding of the complex interactions within biological systems and faster hypothesis generation. New biomedical research articles are published daily and text mining tools are only as good as the corpus from which they work. Many text mining tools are underused because their results are static and do not reflect the constantly expanding knowledge in the field. In order for biomedical text mining to become an indispensable tool used by researchers, this problem must be addressed. To this end, we present PubRunner, a framework for regularly running text mining tools on the latest publications. PubRunner is lightweight, simple to use, and can be integrated with an existing text mining tool. The workflow involves downloading the latest abstracts from PubMed, executing a user-defined tool, pushing the resulting data to a public FTP or Zenodo dataset, and publicizing the location of these results on the public PubRunner website. We illustrate the use of this tool by re-running the commonly used word2vec tool on the latest PubMed abstracts to generate up-to-date word vector representations for the biomedical domain. This shows a proof of concept that we hope will encourage text mining developers to build tools that truly will aid biologists in exploring the latest publications.
Language	English
Publishing date	2017
Publishing country	England
Document type	Journal Article
ZDB-ID	2699932-8
ISSN	2046-1402
ISSN	2046-1402
DOI	10.12688/f1000research.11389.2
Database	MEDical Literature Analysis and Retrieval System OnLINE

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