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Identification of genes related to mental disorders by text mining.

ABSTRACT: Mental disorders are important diseases with a high prevalence rate in the general population. Common mental disorders are complex diseases with high heritability, and their pathogenesis is the result of interactions between genetic and environmental factors. However, the relationship between mental disorders and genes is complex and difficult to evaluate. Additionally, some mental disorders involve numerous genes, and a single gene can also be associated with different types of mental disorders.This study used text mining (including word frequency analysis, cluster analysis, and association analysis) of the PubMed database to identify genes related to mental disorders.Word frequency analysis revealed 52 high-frequency genes important in studies of mental disorders. Cluster analysis showed that 5-HTT, SLC6A4, and MAOA are common genetic factors in most mental disorders; the intra-group genes in each cluster were highly correlated. Some mental disorders may have common genetic factors; for example, there may be common genetic factors between 'Affective Disorders' and 'Schizophrenia.' Association analysis revealed 35 frequent itemsets and 25 association rules, indicating close associations among genes. The results of association rules showed that CCK, MAOA, and 5-HTT are the most closely related.We used text mining technology to analyze genes related to mental disorders to further summarize and clarify the relationships between mental disorders and genes as well as identify potential relationships, providing a foundation for future experiments. The results of the associative analysis also provide a reference for multi-gene studies of mental disorders.

SUBMITTER: Wu Y

PROVIDER: S-EPMC6824703 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Identification of genes related to mental disorders by text mining.

Wu Ying Y Dang Meilin M Li Hongxia H Jin Xing X Yang Wenxiao W

Medicine 20191001 42

Mental disorders are important diseases with a high prevalence rate in the general population. Common mental disorders are complex diseases with high heritability, and their pathogenesis is the result of interactions between genetic and environmental factors. However, the relationship between mental disorders and genes is complex and difficult to evaluate. Additionally, some mental disorders involve numerous genes, and a single gene can also be associated with different types of mental disorders ...[more]

PMID: 31626105

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Project description:BACKGROUND:Some medications carry increased risk of patient harm when they are given in error. In incident reports, names of the medications that are involved in errors could be found written both in a specific medication field and/or within the free text description of the incident. Analysing only the names of the medications implicated in a specific unstructured medication field does not give information of the associated factors and risk areas, but when analysing unstructured free text descriptions, the information about the medication involved and associated risk factors may be buried within other non-relevant text. Thus, the aim of this study was to extract medication names most commonly used in free text descriptions of medication administration incident reports to identify terms most frequently associated with risk for each of these medications using text mining. METHOD:Free text descriptions of medication administration incidents (n = 72,390) reported in 2016 to the National Reporting and Learning System for England and Wales were analysed using SAS® Text miner. Analysis included text parsing and filtering free text to identify most commonly mentioned medications, followed by concept linking, and clustering to identify terms associated with commonly mentioned medications and the associated risk areas. RESULTS:The following risk areas related to medications were identified: 1. Allergic reactions to antibacterial drugs, 2. Intravenous administration of antibacterial drugs, 3. Fentanyl patches, 4. Checking and documenting of analgesic doses, 5. Checking doses of anticoagulants, 6. Insulin doses and blood glucose, 7. Administration of intravenous infusions. CONCLUSIONS:Interventions to increase medication administration safety should focus on checking patient allergies and medication doses, especially for intravenous and transdermal medications. High-risk medications include insulin, analgesics, antibacterial drugs, anticoagulants, and potassium chloride. Text mining may be useful for analysing large free text datasets and should be developed further.

Dataset Information

Identification of genes related to mental disorders by text mining.

Publications

Identification of genes related to mental disorders by text mining.

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