| 1. |
- Ahmed, Rafsan, et al.
(författare)
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EasyNER: A Customizable Easy-to-Use Pipeline for Deep Learning- and Dictionary-based Named Entity Recognition from Medical Text
- 2023
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Medical research generates a large number of publications with the PubMed database already containing >35 million research articles. Integration of the knowledge scattered across this large body of literature could provide key insights into physiological mechanisms and disease processes leading to novel medical interventions. However, it is a great challenge for researchers to utilize this information in full since the scale and complexity of the data greatly surpasses human processing abilities. This becomes especially problematic in cases of extreme urgency like the COVID-19 pandemic. Automated text mining can help extract and connect information from the large body of medical research articles. The first step in text mining is typically the identification of specific classes of keywords (e.g., all protein or disease names), so called Named Entity Recognition (NER). Here we present an end-to-end pipeline for NER of typical entities found in medical research articles, including diseases, cells, chemicals, genes/proteins, and species. The pipeline can access and process large medical research article collections (PubMed, CORD-19) or raw text and incorporates a series of deep learning models fine-tuned on the HUNER corpora collection. In addition, the pipeline can perform dictionary-based NER related to COVID-19 and other medical topics. Users can also load their own NER models and dictionaries to include additional entities. The output consists of publication-ready ranked lists and graphs of detected entities and files containing the annotated texts. An associated script allows rapid inspection of the results for specific entities of interest. As model use cases, the pipeline was deployed on two collections of autophagy-related abstracts from PubMed and on the CORD19 dataset, a collection of 764 398 research article abstracts related to COVID-19.
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| 2. |
- Arvidsson, Malou, et al.
(författare)
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An annotated high-content fluorescence microscopy dataset with Hoechst 33342-stained nuclei and manually labelled outlines
- 2023
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Ingår i: Data in Brief. - : Elsevier BV. - 2352-3409. ; 46
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Tidskriftsartikel (refereegranskat)abstract
- Automated detection of cell nuclei in fluorescence microscopy images is a key task in bioimage analysis. It is essential for most types of microscopy-based high-throughput drug and genomic screening and is often required in smaller scale experiments as well. To develop and evaluate algorithms and neural networks that perform instance or semantic segmentation for detecting nuclei, high quality annotated data is essential. Here we present a benchmarking dataset of fluorescence microscopy images with Hoechst 33342-stained nuclei together with annotations of nuclei, nuclear fragments and micronuclei. Images were randomly selected from an RNA interference screen with a modified U2OS osteosarcoma cell line, acquired on a Thermo Fischer CX7 high-content imaging system at 20x magnification. Labelling was performed by a single annotator and reviewed by a biomedical expert. The dataset, called Aitslab-bioimaging1, contains 50 images showing over 2000 labelled nuclear objects in total, which is sufficiently large to train well-performing neural networks for instance or semantic segmentation. The dataset is split into training, development and test set for user convenience.
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| 3. |
- Arvidsson, Malou, et al.
(författare)
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An annotated high-content fluorescence microscopy dataset with Hoechst 33342-stained nuclei and manually labelled outlines : Dataset record
- 2022
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Here we present a benchmarking dataset of fluorescence microscopy images with Hoechst 33342-stained nuclei together with annotations of nuclei, nuclear fragments and micronuclei. Images were randomly selected from an RNA interference screen with a modified U2OS osteosarcoma cell line, acquired on a Thermo Fischer CX7 high-content imaging system at 20x magnification. Labelling was performed by a single annotator and reviewed by a biomedical expert.The dataset contains 50 images showing over 2000 labelled nuclear objects in total, which is sufficiently large to train well-performing neural networks for instance or semantic segmentation. It is pre-split into training, development and test set, each in a zip file. The dataset should be referred to as Aitslab_bioimaging1. A preprint of a brief article describing the dataset is also available from zenodo (Arvidsson M, Kazemi Rashed S, Aits S. zenodo 2022, https://doi.org/10.1016/j.dib.2022.108769)
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| 4. |
- Kazemi Rashed, Salma, et al.
(författare)
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English dictionaries, gold and silver standard corpora for biomedical natural language processing related to SARS-CoV-2 and COVID-19
- 2020
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Here we present a toolbox for natural language processing tasks related to SARS-CoV-2. It comprises English dictionaries of synonyms for SARS-CoV-2 and COVID-19, a silver standard corpus generated with the dictionaries and a gold standard corpus of 10 Pubmed abstracts manually annotated for disease, virus, symptom and protein/gene terms. This toolbox is freely available on github and can be used for text analytics in a variety of settings related to the COVID-19 crisis. It will be expanded and applied in NLP tasks over the next weeks and the community is invited to contribute.
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| 5. |
- Kazemi Rashed, Salma, et al.
(författare)
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Files and code for English dictionaries, gold and silver standard corpora for biomedical natural language processing related to SARS-CoV-2 and COVID-19 : Dataset record
- 2022
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- BACKGROUNDAutomated information extraction with natural language processing (NLP) tools is required to gain systematic insights from the large number of COVID-19 publications, reports and social media posts, which far exceed human processing capabilities. A key challenge for NLP is the extensive variation in terminology used to describe medical entities, which was especially pronounced for this newly emergent disease.FINDINGSHere we present an NLP toolbox comprising very large English dictionaries of synonyms for SARS-CoV-2 (including variant names) and COVID-19, which can be used with dictionary-based NLP tools. We also present a silver standard corpus generated with the dictionaries, and a gold standard corpus, consisting of PubMed abstracts manually annotated for disease, virus, symptom, protein/gene, cell type, chemical and species terms, which can be used to train and evaluate COVID-19-related NLP tools. Code for annotation, which can be used to expand the silver standard corpus or for text mining is also included. This toolbox is freely available on Github (on https://github.com/Aitslab/corona) and here.CONCLUSIONSThe toolbox can be used for a variety of text analytics tasks related to the COVID-19 crisis and has already been used to create a COVID-19 knowledge graph, study the variability and evolution of COVID-19-related terminology and develop and benchmark text mining tools.
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