Dataset Information

Plant phenotype relationship corpus for biomedical relationships between plants and phenotypes.

ABSTRACT: Medicinal plants have demonstrated therapeutic potential for applicability for a wide range of observable characteristics in the human body, known as "phenotype," and have been considered favorably in clinical treatment. With an ever increasing interest in plants, many researchers have attempted to extract meaningful information by identifying relationships between plants and phenotypes from the existing literature. Although natural language processing (NLP) aims to extract useful information from unstructured textual data, there is no appropriate corpus available to train and evaluate the NLP model for plants and phenotypes. Therefore, in the present study, we have presented the plant-phenotype relationship (PPR) corpus, a high-quality resource that supports the development of various NLP fields; it includes information derived from 600 PubMed abstracts corresponding to 5,668 plant and 11,282 phenotype entities, and demonstrates a total of 9,709 relationships. We have also described benchmark results through named entity recognition and relation extraction systems to verify the quality of our data and to show the significant performance of NLP tasks in the PPR test set.

SUBMITTER: Cho H

PROVIDER: S-EPMC9135735 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

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Plant phenotype relationship corpus for biomedical relationships between plants and phenotypes.

Cho Hyejin H Kim Baeksoo B Choi Wonjun W Lee Doheon D Lee Hyunju H

Scientific data 20220526 1

Medicinal plants have demonstrated therapeutic potential for applicability for a wide range of observable characteristics in the human body, known as "phenotype," and have been considered favorably in clinical treatment. With an ever increasing interest in plants, many researchers have attempted to extract meaningful information by identifying relationships between plants and phenotypes from the existing literature. Although natural language processing (NLP) aims to extract useful information fr ...[more]

PMID: 35618736

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Project description:BackgroundMany new medicines have been derived from natural sources such as plants, which have a long history of being used for disease treatment. Thus, their benefits and side effects have been studied, and plant-related information including plant and disease relations have been accumulated in Medline articles. Because numerous articles are available in Medline and are written in natural language, text-mining is important. However, a corpus of plant and disease relations is not available yet. Thus, we aimed to construct such a corpus.Methods and resultsIn this study, we designed and annotated a plant-disease relations corpus, and proposed a computational model to predict plant-disease relations using the corpus. We categorized plant and disease relations into four types: treatments of diseases, causes of diseases, associations, and negative relations. To construct a corpus of plant-disease relations, we first created its annotation guidelines and randomly selected 200 Medline abstracts. From these abstracts, we identified 1,405 and 1,755 plant and disease mentions, annotated to 105 and 237 unique plant and disease identifiers, respectively. When we selected sentences containing at least one plant and one disease mention, we extracted 878 plant and 1,077 disease entities, which finally generated a corpus of plant-disease relations including 1,309 relations from 199 abstracts. To verify the effectiveness of the corpus, we proposed a convolutional neural network model with the shortest dependency path (SDP-CNN) and applied it to the constructed corpus. The micro F-score with ten-fold cross-validation was found to be 0.764. We also applied the proposed SDP-CNN model to all Medline abstracts. When we measured its performance for 483 randomly selected plant-disease co-occurring sentences, the model showed a precision of 0.707.ConclusionThe plant-disease relations corpus is unique and represents an important resource for biomedical text-mining. The corpus of plant and disease relations is available at http://gcancer.org/pdr/.

Project description:BackgroundAdverse events induced by drug-drug interactions are a major concern in the United States. Current research is moving toward using electronic health record (EHR) data, including for adverse drug events discovery. One of the first steps in EHR-based studies is to define a phenotype for establishing a cohort of patients. However, phenotype definitions are not readily available for all phenotypes. One of the first steps of developing automated text mining tools is building a corpus. Therefore, this study aimed to develop annotation guidelines and a gold standard corpus to facilitate building future automated approaches for mining phenotype definitions contained in the literature. Furthermore, our aim is to improve the understanding of how these published phenotype definitions are presented in the literature and how we annotate them for future text mining tasks.ResultsTwo annotators manually annotated the corpus on a sentence-level for the presence of evidence for phenotype definitions. Three major categories (inclusion, intermediate, and exclusion) with a total of ten dimensions were proposed characterizing major contextual patterns and cues for presenting phenotype definitions in published literature. The developed annotation guidelines were used to annotate the corpus that contained 3971 sentences: 1923 out of 3971 (48.4%) for the inclusion category, 1851 out of 3971 (46.6%) for the intermediate category, and 2273 out of 3971 (57.2%) for exclusion category. The highest number of annotated sentences was 1449 out of 3971 (36.5%) for the "Biomedical & Procedure" dimension. The lowest number of annotated sentences was 49 out of 3971 (1.2%) for "The use of NLP". The overall percent inter-annotator agreement was 97.8%. Percent and Kappa statistics also showed high inter-annotator agreement across all dimensions.ConclusionsThe corpus and annotation guidelines can serve as a foundational informatics approach for annotating and mining phenotype definitions in literature, and can be used later for text mining applications.

Project description:The practice of precision medicine will ultimately require databases of genes and mutations for healthcare providers to reference in order to understand the clinical implications of each patient's genetic makeup. Although the highest quality databases require manual curation, text mining tools can facilitate the curation process, increasing accuracy, coverage, and productivity. However, to date there are no available text mining tools that offer high-accuracy performance for extracting such triplets from biomedical literature. In this paper we propose a high-performance machine learning approach to automate the extraction of disease-gene-variant triplets from biomedical literature. Our approach is unique because we identify the genes and protein products associated with each mutation from not just the local text content, but from a global context as well (from the Internet and from all literature in PubMed). Our approach also incorporates protein sequence validation and disease association using a novel text-mining-based machine learning approach. We extract disease-gene-variant triplets from all abstracts in PubMed related to a set of ten important diseases (breast cancer, prostate cancer, pancreatic cancer, lung cancer, acute myeloid leukemia, Alzheimer's disease, hemochromatosis, age-related macular degeneration (AMD), diabetes mellitus, and cystic fibrosis). We then evaluate our approach in two ways: (1) a direct comparison with the state of the art using benchmark datasets; (2) a validation study comparing the results of our approach with entries in a popular human-curated database (UniProt) for each of the previously mentioned diseases. In the benchmark comparison, our full approach achieves a 28% improvement in F1-measure (from 0.62 to 0.79) over the state-of-the-art results. For the validation study with UniProt Knowledgebase (KB), we present a thorough analysis of the results and errors. Across all diseases, our approach returned 272 triplets (disease-gene-variant) that overlapped with entries in UniProt and 5,384 triplets without overlap in UniProt. Analysis of the overlapping triplets and of a stratified sample of the non-overlapping triplets revealed accuracies of 93% and 80% for the respective categories (cumulative accuracy, 77%). We conclude that our process represents an important and broadly applicable improvement to the state of the art for curation of disease-gene-variant relationships.

Dataset Information

Plant phenotype relationship corpus for biomedical relationships between plants and phenotypes.

Publications

Plant phenotype relationship corpus for biomedical relationships between plants and phenotypes.

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