Design of the formalized and integrated Alzheimer's Disease Ontology and its application in retrieving textual data via text mining.

As one of the leading causes for dementia in the population, it is imperative that we discern exactly why Alzheimer's disease (AD) has a strong molecular association with beta-amyloid and tau. Although a clear understanding about etiology and pathogenesis of AD remains unsolved, scientists worldwide have dedicated significant efforts to discovering the molecular interactions linked to the pathological characteristics and potential treatments. Knowledge representations, such as domain ontologies encompassing our current understanding about AD, could greatly assist and contribute to disease research. This paper describes the construction and application of the integrated Alzheimer's Disease Ontology (ADO), combining selected concepts from the former version of the ADO and the Alzheimer's Disease Mapping Ontology (ADMO). In addition to the existing entities available from these knowledge models, essential knowledge about AD from public sources, such as newly discovered risk factor genes and novel treatments, was also integrated. The ADO can also be leveraged in text mining scenarios given that it is conceptually enriched with domain-specific knowledge as well as their relations. The integrated ADO consists of 39 855 total axioms. The ontology covers many aspects of the AD domain, including risk factor genes, clinical features, treatments and experimental models. The ontology complies with the Open Biological and Biomedical Ontology principles and was accepted by the foundry. In this paper, we illustrate the role of the presented ontology in extracting textual information from the SCAIView database and key measures in an ADO-based corpus. Database URL:  https://academic.oup.com/database.

Association between the rs6313 polymorphism in the 5-HTR2A gene and the efficacy of antipsychotic drugs.

BACKGROUND: Prescribing the optimal antipsychotic treatment to schizophrenia is very important as it is well established that patients have different sensitivity to the available antipsychotic drugs. The genotype of the HTR2A T102C (rs6313) polymorphism has been suggested to affect the efficacy of antipsychotic drugs, but the results of different studies have been inconsistent METHODS: In this study, a meta-analysis was used to ascertain the association between allele and genotype polymorphism of rs6313 and the efficacy of antipsychotic drugs. Related studies publicated from January 1995 to December 2021 were retrieved from PubMed, Embase, ScienceDirect, and Web of Science databases. The correlations between allele and genotype polymorphism of rs6313 and the responder rate and scale score reduction rate of antipsychotics were analyzed. In addition, subgroup analyses were performed on time, drug, and ethnicity. RESULTS: A total of 18 studies were included. The meta-analysis showed that allele and genotype polymorphisms at the rs6313 locus overall were not associated with antipsychotic drug responder rate or scale score reduction rate. Ethnicity subgroup analysis showed that antipsychotic drugs were more effective in patients with allele T in the Caucasian population. Indian patients with the TT genotype had the lowest scale score reduction rate and poor drug treatment effect. East Asian patients with the TC genotype had better treatment effect, whereas in patients with the CC genotype, the treatment was less effective. Drug subgroup analysis showed that patients with the TC genotype treated with clozapine had the highest responder rate and score reduction rate. CONCLUSIONS: The association between rs6313 polymorphism and the efficacy of antipsychotic drugs is mainly influenced by drug and ethnicity. Caucasian patients with the T allele respond better to drug therapy, and Asian patients with TC genotype. The TC genotype was also a good predictor of the efficacy of clozapine treatment.

Common data model for COVID-19 datasets.

MOTIVATION: A global medical crisis like the coronavirus disease 2019 (COVID-19) pandemic requires interdisciplinary and highly collaborative research from all over the world. One of the key challenges for collaborative research is a lack of interoperability among various heterogeneous data sources. Interoperability, standardization and mapping of datasets are necessary for data analysis and applications in advanced algorithms such as developing personalized risk prediction modeling. RESULTS: To ensure the interoperability and compatibility among COVID-19 datasets, we present here a common data model (CDM) which has been built from 11 different COVID-19 datasets from various geographical locations. The current version of the CDM holds 4639 data variables related to COVID-19 such as basic patient information (age, biological sex and diagnosis) as well as disease-specific data variables, for example, Anosmia and Dyspnea. Each of the data variables in the data model is associated with specific data types, variable mappings, value ranges, data units and data encodings that could be used for standardizing any dataset. Moreover, the compatibility with established data standards like OMOP and FHIR makes the CDM a well-designed CDM for COVID-19 data interoperability. AVAILABILITY AND IMPLEMENTATION: The CDM is available in a public repo here: https://github.com/Fraunhofer-SCAI-Applied-Semantics/COVID-19-Global-Model. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.