Set Of Annotations Research Articles

The Arabidopsis Information Resource in 2024.

Since 1999, The Arabidopsis Information Resource (www.arabidopsis.org) has been curating data about the Arabidopsis thaliana genome. Its primary focus is integrating experimental gene function information from the peer-reviewed literature and codifying it as controlled vocabulary annotations. Our goal is to produce a "gold standard" functional annotation set that reflects the current state of knowledge about the Arabidopsis genome. At the same time, the resource serves as a nexus for community-based collaborations aimed at improving data quality, access, and reuse. For the past decade, our work has been made possible by subscriptions from our global user base. This update covers our ongoing biocuration work, some of our modernization efforts that contribute to the first major infrastructure overhaul since 2011, the introduction of JBrowse2, and the resource's role in community activities such as organizing the structural reannotation of the genome. For gene function assessment, we used gene ontology annotations as a metric to evaluate: (1) what is currently known about Arabidopsis gene function and (2) the set of "unknown" genes. Currently, 74% of the proteome has been annotated to at least one gene ontology term. Of those loci, half have experimental support for at least one of the following aspects: molecular function, biological process, or cellular component. Our work sheds light on the genes for which we have not yet identified any published experimental data and have no functional annotation. Drawing attention to these unknown genes highlights knowledge gaps and potential sources of novel discoveries.

Prognosis and biological function of SGOL1 in clear cell renal cell carcinoma: a multiomics analysis.

Shugoshin-1 (SGOL1) is a mammalian ortholog of Shugoshin in yeast and is essential for precise chromosome segregation during mitosis and meiosis. Aberrant SGOL1 expression was reported to be closely correlated with the malignant progression of various tumors. However, the expression pattern and biological function of SGOL1 in clear cell renal cell carcinoma (ccRCC) are unclear. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases provide mRNA expression data and outcome information for ccRCC patients. Immunohistochemistry (IHC) of ccRCC tissue chips verified SGOL1 protein expression in ccRCC patients. Data processing and visualization were performed with the UALCAN, TISIDB, TIMER, GSCA, LinkedOmics, and starBase databases. Gene Ontology (GO) annotation and gene set enrichment analysis (GSEA) were used to identify SGOL1-related biological functions and signaling pathways. Immune infiltration analysis was performed using the TISIDB database, ssGSEA algorithm, and TCGA-KIRC cohort. The biological role of SGOL1 in ccRCC was investigated using a series of in vitro cytological assays, including the MTT assay, EdU staining assay, flow cytometry analysis, Transwell assay, and wound healing assay. SGOL1 was highly expressed in ccRCC and linked to adverse clinicopathological parameters and unfavorable prognosis. Multivariate logistic regression and nomogram calibration suggested that SGOL1 might serve as an independent and reliable prognostic predictor of ccRCC. Functional enrichment analysis indicated that SGOL1 may be involved in the cell cycle, the p53 pathway, DNA replication, and T-cell activation. Furthermore, tumor microenvironment (TME) analysis suggested that SGOL1 was positively associated with Treg infiltration and immune checkpoint upregulation. In addition, we identified a potential SNHG17/PVT1/ZMIZ1-AS1-miR-23b-3p-SGOL1 axis correlated with ccRCC carcinogenesis and progression. Finally, we demonstrated that SGOL1 promoted ccRCC cell proliferation, migratory capacity, and invasion in vitro. SGOL1 potentially functions as an oncogene in ccRCC progression and might contribute to the immunosuppressive TME by increasing Treg infiltration and checkpoint expression, suggesting that targeting SGOL1 could be a novel therapeutic strategy for the treatment of ccRCC patients.

Neural network approach for shape-based euhedral pyrite identification in X-ray CT data with adversarial unsupervised domain adaptation

We explore an attenuation and shape-based identification of euhedral pyrites in high-resolution X-ray Computed Tomography (XCT) data using deep neural networks. To deal with the scarcity of annotated data we generate a complementary training set of synthetic images. To investigate and address the domain gap between the synthetic and XCT data, several deep learning models, with and without domain adaption, are trained and compared. We find that a model trained on a small set of human annotations, while displaying over-fitting, can rival the human annotators. The unsupervised domain adaptation approaches are successful in bridging the domain gap, which significantly improves their performance. A domain-adapted model, trained on a dataset that fuses synthetic and real data, is the overall best-performing model. This highlights the possibility of using synthetic datasets for the application of deep learning in mineralogy.

ToxCodAn-Genome: an automated pipeline for toxin-gene annotation in genome assembly of venomous lineages.

The rapid development of sequencing technologies resulted in a wide expansion of genomics studies using venomous lineages. This facilitated research focusing on understanding the evolution of adaptive traits and the search for novel compounds that can be applied in agriculture and medicine. However, the toxin annotation of genomes is a laborious and time-consuming task, and no consensus pipeline is currently available. No computational tool currently exists to address the challenges specific to toxin annotation and to ensure the reproducibility of the process. Here, we present ToxCodAn-Genome, the first software designed to perform automated toxin annotation in genomes of venomous lineages. This pipeline was designed to retrieve the full-length coding sequences of toxins and to allow the detection of novel truncated paralogs and pseudogenes. We tested ToxCodAn-Genome using 12 genomes of venomous lineages and achieved high performance on recovering their current toxin annotations. This tool can be easily customized to allow improvements in the final toxin annotation set and can be expanded to virtually any venomous lineage. ToxCodAn-Genome is fast, allowing it to run on any personal computer, but it can also be executed in multicore mode, taking advantage of large high-performance servers. In addition, we provide a guide to direct future research in the venomics field to ensure a confident toxin annotation in the genome being studied. As a case study, we sequenced and annotated the toxin repertoire of Bothrops alternatus, which may facilitate future evolutionary and biomedical studies using vipers as models. ToxCodAn-Genome is suitable to perform toxin annotation in the genome of venomous species and may help to improve the reproducibility of further studies. ToxCodAn-Genome and the guide are freely available at https://github.com/pedronachtigall/ToxCodAn-Genome.

Deep convolutional neural network and IoT technology for healthcare.

Deep convolutional neural network and IoT technology for healthcare.

Sparse annotation learning for dense volumetric MR image segmentation with uncertainty estimation

Objective. Training neural networks for pixel-wise or voxel-wise image segmentation is a challenging task that requires a considerable amount of training samples with highly accurate and densely delineated ground truth maps. This challenge becomes especially prominent in the medical imaging domain, where obtaining reliable annotations for training samples is a difficult, time-consuming, and expert-dependent process. Therefore, developing models that can perform well under the conditions of limited annotated training data is desirable. Approach. In this study, we propose an innovative framework called the extremely sparse annotation neural network (ESA-Net) that learns with only the single central slice label for 3D volumetric segmentation which explores both intra-slice pixel dependencies and inter-slice image correlations with uncertainty estimation. Specifically, ESA-Net consists of four specially designed distinct components: (1) an intra-slice pixel dependency-guided pseudo-label generation module that exploits uncertainty in network predictions while generating pseudo-labels for unlabeled slices with temporal ensembling; (2) an inter-slice image correlation-constrained pseudo-label propagation module which propagates labels from the labeled central slice to unlabeled slices by self-supervised registration with rotation ensembling; (3) a pseudo-label fusion module that fuses the two sets of generated pseudo-labels with voxel-wise uncertainty guidance; and (4) a final segmentation network optimization module to make final predictions with scoring-based label quantification. Main results. Extensive experimental validations have been performed on two popular yet challenging magnetic resonance image segmentation tasks and compared to five state-of-the-art methods. Significance. Results demonstrate that our proposed ESA-Net can consistently achieve better segmentation performances even under the extremely sparse annotation setting, highlighting its effectiveness in exploiting information from unlabeled data.

Decision support for detecting sensitive text in government records

AbstractFreedom of information laws promote transparency by permitting individuals and organizations to obtain government documents. However, exemptions from disclosure are necessary to protect privacy and to permit government officials to deliberate freely. Deliberative language is often the most challenging and burdensome exemption to detect, leading to high processing costs and delays in responding to open-records requests. This paper describes a novel deliberative-language detection model trained on a new annotated training set. The deliberative-language detection model is a component of a decision-support system for open-records requests under the US Freedom of Information Act, the FOIA Assistant, that ingests documents responsive to an open-records requests, suggests passages likely to be subject to deliberative language, privacy, or other exemptions, and assists analysts in rapidly redacting suggested passages. The tool’s interface is based on extensive human-factors and usability studies with analysts and is currently in operational testing by multiple US federal agencies.

The role of noise in denoising models for anomaly detection in medical images.

Pathological brain lesions exhibit diverse appearance in brain images, in terms of intensity, texture, shape, size, and location. Comprehensive sets of data and annotations are difficult to acquire. Therefore, unsupervised anomaly detection approaches have been proposed using only normal data for training, with the aim of detecting outlier anomalous voxels at test time. Denoising methods, for instance classical denoising autoencoders (DAEs) and more recently emerging diffusion models, are a promising approach, however naive application of pixelwise noise leads to poor anomaly detection performance. We show that optimization of the spatial resolution and magnitude of the noise improves the performance of different model training regimes, with similar noise parameter adjustments giving good performance for both DAEs and diffusion models. Visual inspection of the reconstructions suggests that the training noise influences the trade-off between the extent of the detail that is reconstructed and the extent of erasure of anomalies, both of which contribute to better anomaly detection performance. We validate our findings on two real-world datasets (tumor detection in brain MRI and hemorrhage/ischemia/tumor detection in brain CT), showing good detection on diverse anomaly appearances. Overall, we find that a DAE trained with coarse noise is a fast and simple method that gives state-of-the-art accuracy. Diffusion models applied to anomaly detection are as yet in their infancy and provide a promising avenue for further research. Code for our DAE model and coarse noise is provided at: https://github.com/AntanasKascenas/DenoisingAE.

Association of low frequency variants with regional cortical grey matter volumes in genetic frontotemporal dementia: Results from GENFI

AbstractBackgroundFrontotemporal dementia (FTD) is a neurodegenerative condition characterized by heterogeneous clinical, pathological, and genetic features. Mutations in three genes account for the majority of autosomal dominant FTD: GRN, MAPT, and C9orf72. We tested whether gene‐based aggregate burden of genome‐wide low‐frequency variants contribute to variation in regional cortical and subcortical grey matter volumes in the GENetic Frontotemporal dementia Initiative (GENFI), after controlling for effects of autosomal dominant mutations.MethodGENFI recruits symptomatic and presymptomatic participants from families segregating genetic FTD. We included 517 participants with genotype (Neurochip; imputed against TOPMed), and T1w‐MRI brain volumetric data. Gene‐based burden tests that aggregate the number of uncommon/rare variants by gene were used to examine the association of low‐frequency variants (minor allele frequency: 0.000001 to <0.05) with regional cortical and subcortical grey matter volumes, controlling for age, sex, total intracranial volume, mutation status, population stratification, and family membership (kinship matrix). In two separate analyses, we used the following set of annotations to account for (i) loss of function mutations (LOF): start gain, stop loss, start loss, essential splice site, stop gain, normal splice site, and non‐synonymous, (ii) insertions, deletions, and frameshift mutations (indel‐fs).ResultOf the 517 participants (300 women), 307 were mutation carriers (symptomatic = 82). For LOF mutations (731 genes tested; significance threshold: p = 0.05/731 = 6.8×10−5), aggregate burden of variants in BSND were associated with lower volumes in: temporal (p = 6.6×10−6), left insula (p = 5.0×10−5), and ventromedial prefrontal cortical (p = 2.8×10−5) regions. For indel‐fs mutations (826 genes tested; significance threshold: p = 0.05/826 = 6.05×10−5), aggregate burden of variants in: (i) E2F2 was associated with lower volumes in occipital (p = 8.7×10−6) regions; (ii) TMEM61 was associated with lower volumes in left temporal (p = 7.8×10−7), and left cingulate regions (p = 5.5×10−5); (iii) KDM4A‐AS1 was associated with lower volumes in the right temporal (p = 3.2×10‐7) region. All genes are variably expressed in the brain.ConclusionIdentification of deleterious or protective low‐frequency variants contributing to FTD imaging phenotypes may help identify genetic modifiers of familial FTD. Replication of results followed by functional studies are needed.

Quality aspects of annotated data

The quality of Machine Learning (ML) applications is commonly assessed by quantifying how well an algorithm fits its respective training data. Yet, a perfect model that learns from and reproduces erroneous data will always be flawed in its real-world application. Hence, a comprehensive assessment of ML quality must include an additional data perspective, especially for models trained on human-annotated data. For the collection of human-annotated training data, best practices often do not exist and leave researchers to make arbitrary decisions when collecting annotations. Decisions about the selection of annotators or label options may affect training data quality and model performance.In this paper, I will outline and summarize previous research and approaches to the collection of annotated training data. I look at data annotation and its quality confounders from two perspectives: the set of annotators and the strategy of data collection. The paper will highlight the various implementations of text and image annotation collection and stress the importance of careful task construction. I conclude by illustrating the consequences for future research and applications of data annotation. The paper is intended give readers a starting point on annotated data quality research and stress the necessity of thoughtful consideration of the annotation collection process to researchers and practitioners.

Unraveling Connective Tissue Growth Factor as a Therapeutic Target and Assessing Kahweol as a Potential Drug Candidate in Triple-Negative Breast Cancer Treatment.

Triple-negative breast cancer (TNBC) is characterized by aggressive behavior and limited treatment options, necessitating the identification of novel therapeutic targets. In this study, we investigated the clinical significance of connective tissue growth factor (CTGF) as a prognostic marker and explored the potential therapeutic effects of kahweol, a coffee diterpene molecule, in TNBC treatment. Initially, through a survival analysis on breast cancer patients from The Cancer Genome Atlas (TCGA) database, we found that CTGF exhibited significant prognostic effects exclusively in TNBC patients. To gain mechanistic insights, we performed the functional annotation and gene set enrichment analyses, revealing the involvement of CTGF in migratory pathways relevant to TNBC treatment. Subsequently, in vitro experiments using MDA-MB 231 cells, a representative TNBC cell line, demonstrated that recombinant CTGF (rCTGF) administration enhanced cell motility, whereas CTGF knockdown using CTGF siRNA resulted in reduced motility. Notably, rCTGF restored kahweol-reduced cell motility, providing compelling evidence for the role of CTGF in mediating kahweol's effects. At the molecular level, kahweol downregulated the protein expression of CTGF as well as critical signaling molecules, such as p-ERK, p-P38, p-PI3K/AKT, and p-FAK, associated with cell motility. In summary, our findings propose CTGF as a potential prognostic marker for guiding TNBC treatment and suggest kahweol as a promising antitumor compound capable of regulating CTGF expression to suppress cell motility in TNBC. These insights hold promise for the development of targeted therapies and improved clinical outcomes for TNBC patients.

Semantic pattern recognition of special English words based on data corpus technology

Abstract This paper first researches special English vocabulary teaching defines special English vocabulary definitions and characteristics based on special English usage, and proposes strategies for learning special English vocabulary. The study involves studying the semantic recognition of special English using data corpus technology. Based on data corpus technology, the semantic structure of English vocabulary is analyzed according to Chinese analysis and word vectors, and to facilitate the extraction of English semantic features, IOB2 labeled training corpus is needed. Then, the CRFs network algorithm in the data corpus technology is used to construct the special English semantic recognition model, the lexical annotation set is refined on the preprocessing, and at the same time, the semantic features are added to the recognition model of CRFs mainly used to recognize the noun phrases of gerund class and the experimental analysis is carried out on the recognition of the semantic patterns of the special English vocabulary. The results show that among high school students with high English semantic recognition ability, the correlation coefficient between their aggregates and reading level is 0.753, the correlation coefficient between collocated phrases and reading level is 0.772, and there is a high correlation between the ability to recognize aggregates, collocated phrases, and sentence framing phrases and reading level. This study is designed to improve the efficiency of English reading and communication and has a facilitatory effect on recognizing English lexical semantic patterns.

A Gene Set-Integrated Approach for Predicting Disease-Associated Genes.

It is important to identify disease-associated genes for studying the pathogenic mechanism of complex diseases. Recently, models for disease gene prediction are dominantly based on molecular expression data and networks, including gene expression, protein expression, co-expression networks, protein-protein interaction networks, etc. One limitation of these methods is that they do not consider the knowledge of annotated gene sets representing known pathways or functionally-related sets of genes. In this study, we propose a new approach to predict disease-associated genes by integrating annotated gene sets data from the Molecular Signature Database (MSigDB). It first represents and integrates the different types of annotated gene sets in the MSigDB database in the form of the signal matrix. It then uses the signal matrix as the gene feature to train the disease gene prediction model. We compare our method with existing methods in predicting genes for five complex diseases. The results show that our method is superior to other methods. Further, we perform a case study on autism spectrum disorder (ASD). We find that ASD predictions are associated with ASD based on the statistical analysis of biological networks and independent ASD studies. The source code, prediction results and datasets are publicly available on https://github.com/genemine/GSI.git.

Analysis of Artificial Intelligence-Based Automated Phenotyping of Coronary Artery Disease Diagnoses for Patients who Received Radiotherapy for Thoracic Malignancies.

Coronary artery disease (CAD) is a critical risk factor and toxicity endpoint in thoracic radiation oncology. The recent advent of artificial intelligence (AI) algorithms - specifically natural language processing (NLP) - allows for automated extraction of this data from electronic health records (EHR) more efficiently and at scale. We report on the application and performance characteristics of an EHR-based automated phenotyping tool to a cohort of lung cancer patients receiving radiation therapy (RT). We applied to this radiation oncology use-case an AI-based disease phenotyping tool developed by other researchers for phenotyping CAD from the EHR. The CAD model was trained on 191,187 patients with at least one ICD code and validated with 158 gold-standard patients (Sensitivity: 73%, Specificity: 93%, PPV: 90%). NLP disease definitions were determined by UMLS and were employed with ICD codes through PheNorm classification method to create the phenotype algorithm (Yue et al., J Am Med Inform Assoc. 2018 Jan 1; 25(1):54-60). We applied the CAD phenotyping tool to two independent cohorts of RT treated lung cancer patients: 1) an annotated set of 677 patients who received RT from 1998 to 2014 and were studied in prior cardiac toxicity research; 2) an independent set of 209 patients who received RT from 1998 to 2018 and were annotated separately. Only 7.7% of the AI training population had a CAD diagnosis code, compared to 32.5% and 65.0% in the 677 patient and 209 patient cohorts, respectively. Validation of the AI predicted CAD is shown in the table below. Performance on the 209 patient cohort best matched the AI training set. The 677-patient cohort differed from the 209-patient cohort in that its CAD definition included patients with radiographic coronary artery calcifications, which accounted for the difference in cohort sensitivities, F-scores, and NPVs. The AI calculated CAD was somewhat accurate in detecting whether a patient had CAD and was very accurate in confirming no CAD. The differences in sensitivity and NPV reflect differences in defining CAD, but this study demonstrates the potential for automated AI-phenotyping to detect pre-treatment risk factors and cardiac toxicity events from the EHR.

Named Entities in Modernist Literary Texts

This paper is a follow-up and elaboration of the paper published in the JTDH 2022 Conference Proceedings on manual semantic annotation of named entities based on a proposed set of annotations for a corpus of modernist literary texts. We first briefly describe the corpus and introduce the annotation scheme, then focus on the results of additional analyses, and conclude with further challenges and issues we identified with respect to established NER systems and practices of related projects. Overall, we identify several categories of proper names, foreign language elements, and bibliographic citations, but focus here on the challenges of annotating names of literary characters and place names, and provide examples of the results of preliminary analyses of these entities in the corpus.

Mitigating bias in facial analysis systems by incorporating label diversity

Facial analysis models are increasingly applied in real-world applications that significantly impact people’s lives. However, as literature has shown, models that automatically classify facial attributes might exhibit algorithmic discrimination behavior concerning protected groups, potentially negatively impacting individuals and society. It is, therefore, critical to develop techniques that can mitigate unintended biases in facial classifiers. Hence, this work introduces a novel learning method combining subjective human-based labels and objective annotations based on mathematical definitions of facial traits. Specifically, we generate new objective annotations from two large-scale human-annotated datasets, each capturing a different perspective of the analyzed facial trait. We then propose an ensemble learning method, which combines individual models trained on different types of annotations. We provide an in-depth analysis of the annotation procedure as well as the datasets distribution. We next show how much each individual model, each trained on a different set of annotations, contributes to the final ensemble. Finally, we empirically show that, by incorporating label diversity in the model pipeline, our method successfully mitigates unintended biases while maintaining significant accuracy on the downstream tasks. Our method is generic and can be applied to different types of facial tasks.

Extracting Complementary and Integrative Health Approaches in Electronic Health Records.

Complementary and Integrative Health (CIH) has gained increasing popularity in the past decades. While the evidence bases to support them are growing, there is still a gap in understanding their effects and potential adverse events using real-world data. The overall goal of this study is to represent information pertinent to both psychological and physical CIH approaches (specifically, using examples of music therapy, chiropractic, and aquatic exercise in this study) in an electronic health record (EHR) system. We also aim to evaluate the ability of existing natural language processing (NLP) systems to identify CIH approaches. A total of 300 notes were randomly selected and manually annotated. Annotations were made for status, symptom, and frequency of each approach. This set of annotations was used as a gold standard to evaluate the performance of NLP systems used in this study (specifically BioMedICUS, MetaMap, and cTAKES) for extracting CIH concepts. Venn diagram was used to investigate the consistency of medical records searching by Current Procedural Terminology (CPT) codes and CIH approaches keywords in SQL. Since CPT codes usually do not have specific mentions of CIH approaches, the Venn diagram had less overlap with those found in clinical notes for all three CIH therapies. The three NLP systems achieved 0.41 in average lenient match F1-score in all three CIH approaches, respectively. BioMedICUS achieved the best performance in aquatic exercise with an F1-score of 0.66. This study contributes to the overall representation of CIH in clinical note and lays a foundation for using EHR for clinical research for CIH approaches.

Deriving Ground Truth Labels for Regression Problems Using Annotator Precision

When training machine learning models with practical applications, a quality ground truth dataset is critical. Unlike in classification problems, there is currently no effective method for determining a single ground truth value or landmark from a set of annotations in regression problems. We propose a novel method for deriving ground truth labels in regression problems that considers the performance and precision of individual annotators when identifying each label separately. In contrast to the commonly accepted method of computing the global mean, our method does not assume each annotator to be equally capable of completing the specified task, but rather ensures that higher-performing annotators have a greater contribution to the final result. The ground truth selection method described within this paper provides a means of improving the quality of input data for machine learning model development by removing lower-quality labels. In this study, we objectively demonstrate the improved performance by applying the method to a simulated dataset where a canonical ground truth position can be known, as well as to a sample of data collected from crowd-sourced labels.

MTMVC: Semi-supervised 3D hand pose estimation using multi-task and multi-view consistency

The high performance of state-of-the-art deep learning methods for 3D hand pose estimation heavily depends on a large annotated training set. However, it is difficult and time-consuming to obtain the annotations for 3D hand poses. To leverage unannotated images to reduce the annotation cost, we propose a semi-supervised method based on Multi-Task and Multi-View Consistency (MTMVC) for hand pose estimation. First, we obtain the joints based on heatmap prediction and coordinate regression parallelly and encourage their consistency. Second, we introduce multi-view consistency to encourage the predicted poses to be rotation-invariant. Thirdly, to make the network pay more attention to the hand region, we propose a spatially weighted consistency. Experiments on four public datasets showed that our proposed MTMVC outperformed existing semi-supervised hand pose estimation methods, and by only using half of the annotations, the accuracy of our method was comparable to those of several state-of-the-art fully supervised methods.

NeuroBridge ontology: computable provenance metadata to give the long tail of neuroimaging data a FAIR chance for secondary use.

Despite the efforts of the neuroscience community, there are many published neuroimaging studies with data that are still not findable or accessible. Users face significant challenges in reusing neuroimaging data due to the lack of provenance metadata, such as experimental protocols, study instruments, and details about the study participants, which is also required for interoperability. To implement the FAIR guidelines for neuroimaging data, we have developed an iterative ontology engineering process and used it to create the NeuroBridge ontology. The NeuroBridge ontology is a computable model of provenance terms to implement FAIR principles and together with an international effort to annotate full text articles with ontology terms, the ontology enables users to locate relevant neuroimaging datasets. Building on our previous work in metadata modeling, and in concert with an initial annotation of a representative corpus, we modeled diagnosis terms (e.g., schizophrenia, alcohol usage disorder), magnetic resonance imaging (MRI) scan types (T1-weighted, task-based, etc.), clinical symptom assessments (PANSS, AUDIT), and a variety of other assessments. We used the feedback of the annotation team to identify missing metadata terms, which were added to the NeuroBridge ontology, and we restructured the ontology to support both the final annotation of the corpus of neuroimaging articles by a second, independent set of annotators, as well as the functionalities of the NeuroBridge search portal for neuroimaging datasets. The NeuroBridge ontology consists of 660 classes with 49 properties with 3,200 axioms. The ontology includes mappings to existing ontologies, enabling the NeuroBridge ontology to be interoperable with other domain specific terminological systems. Using the ontology, we annotated 186 neuroimaging full-text articles describing the participant types, scanning, clinical and cognitive assessments. The NeuroBridge ontology is the first computable metadata model that represents the types of data available in recent neuroimaging studies in schizophrenia and substance use disorders research; it can be extended to include more granular terms as needed. This metadata ontology is expected to form the computational foundation to help both investigators to make their data FAIR compliant and support users to conduct reproducible neuroimaging research.