Consensus Dataset Research Articles

Cheminformatic Identification of Tyrosyl-DNA Phosphodiesterase 1 (Tdp1) Inhibitors: A Comparative Study of SMILES-Based Supervised Machine Learning Models.

Tyrosyl-DNA phosphodiesterase 1 (Tdp1) repairs damages in DNA induced by abortive topoisomerase 1 activity; however, maintenance of genetic integrity may sustain cellular division of neoplastic cells. It follows that Tdp1-targeting chemical inhibitors could synergize well with existing chemotherapy drugs to deny cancer growth; therefore, identification of Tdp1 inhibitors may advance precision medicine in oncology. Current computational research efforts focus primarily on molecular docking simulations, though datasets involving three-dimensional molecular structures are often hard to curate and computationally expensive to store and process. We propose the use of simplified molecular input line entry system (SMILES) chemical representations to train supervised machine learning (ML) models, aiming to predict potential Tdp1 inhibitors. An open-sourced consensus dataset containing the inhibitory activity of numerous chemicals against Tdp1 was obtained from Kaggle. Various ML algorithms were trained, ranging from simple algorithms to ensemble methods and deep neural networks. For algorithms requiring numerical data, SMILES were converted to chemical descriptors using RDKit, an open-sourced Python cheminformatics library. Out of 13 optimized ML models with rigorously tuned hyperparameters, the random forest model gave the best results, yielding a receiver operating characteristics-area under curve of 0.7421, testing accuracy of 0.6815, sensitivity of 0.6444, specificity of 0.7156, precision of 0.6753, and F1 score of 0.6595. Ensemble methods, especially the bootstrap aggregation mechanism adopted by random forest, outperformed other ML algorithms in classifying Tdp1 inhibitors from non-inhibitors using SMILES. The discovery of Tdp1 inhibitors could unlock more treatment regimens for cancer patients, allowing for therapies tailored to the patient's condition.

Exploring the Genetic Landscape of Vitiligo in the Pura Raza Español Horse: A Genomic Perspective.

Vitiligo is a depigmentation autoimmune disorder characterized by the progressive loss of melanocytes leading to the appearance of patchy depigmentation of the skin. The presence of vitiligo in horses is greater in those with grey coats. The aim of this study was therefore to perform a genome-wide association study (GWAS) to identify genomic regions and putative candidate loci associated with vitiligo depigmentation and susceptibility in the Pura Raza Español population. For this purpose, we performed a wssGBLUP (weighted single step genomic best linear unbiased prediction) using data from a total of 2359 animals genotyped with Affymetrix Axiom™ Equine 670 K and 1346 with Equine GeneSeek Genomic Profiler™ (GGP) Array V5. A total of 60,136 SNPs (single nucleotide polymorphisms) present on the 32 chromosomes from the consensus dataset after quality control were employed for the analysis. Vitiligo-like depigmentation was phenotyped by visual inspection of the different affected areas (eyes, mouth, nostrils) and was classified into nine categories with three degrees of severity (absent, slight, and severe). We identified one significant genomic region for vitiligo around the eyes, eight significant genomic regions for vitiligo around the mouth, and seven significant genomic regions for vitiligo around the nostrils, which explained the highest percentage of variance. These significant genomic regions contained candidate genes related to melanocytes, skin, immune system, tumour suppression, metastasis, and cutaneous carcinoma. These findings enable us to implement selective breeding strategies to decrease the incidence of vitiligo and to elucidate the genetic architecture underlying vitiligo in horses as well as the molecular mechanisms involved in the disease's development. However, further studies are needed to better understand this skin disorder in horses.

The state of the dark energy equation of state circa 2023

We critically examine the state of current constraints on the dark energy (DE) equation of state (EoS) w. Our study is motivated by the observation that, while broadly consistent with the cosmological constant value w = -1, several independent probes appear to point towards a slightly phantom EoS (w ∼ -1.03) which, if confirmed, could have important implications for the Hubble tension. We pay attention to the apparent preference for phantom DE from Planck Cosmic Microwave Background (CMB) data alone, whose origin we study in detail and attribute to a wide range of (physical and geometrical) effects. We deem the combination of Planck CMB, Baryon Acoustic Oscillations, Type Ia Supernovae, and Cosmic Chronometers data to be particularly trustworthy, inferring from this final consensus dataset w = -1.013+0.038 -0.043, in excellent agreement with the cosmological constant value. Overall, despite a few scattered hints, we find no compelling evidence forcing us away from the cosmological constant (yet).

AI-Dentify: deep learning for proximal caries detection on bitewing x-ray - HUNT4 Oral Health Study.

Dental caries diagnosis requires the manual inspection of diagnostic bitewing images of the patient, followed by a visual inspection and probing of the identified dental pieces with potential lesions. Yet the use of artificial intelligence, and in particular deep-learning, has the potential to aid in the diagnosis by providing a quick and informative analysis of the bitewing images. A dataset of 13,887 bitewings from the HUNT4 Oral Health Study were annotated individually by six different experts, and used to train three different object detection deep-learning architectures: RetinaNet (ResNet50), YOLOv5 (M size), and EfficientDet (D0 and D1 sizes). A consensus dataset of 197 images, annotated jointly by the same six dental clinicians, was used for evaluation. A five-fold cross validation scheme was used to evaluate the performance of the AI models. The trained models show an increase in average precision and F1-score, and decrease of false negative rate, with respect to the dental clinicians. When compared against the dental clinicians, the YOLOv5 model shows the largest improvement, reporting 0.647 mean average precision, 0.548 mean F1-score, and 0.149 mean false negative rate. Whereas the best annotators on each of these metrics reported 0.299, 0.495, and 0.164 respectively. Deep-learning models have shown the potential to assist dental professionals in the diagnosis of caries. Yet, the task remains challenging due to the artifacts natural to the bitewing images.

Integration of Patient-Reported Outcome Data Collected Via Web Applications and Mobile Apps Into a Nation-Wide COVID-19 Research Platform Using Fast Healthcare Interoperability Resources: Development Study.

The Network University Medicine projects are an important part of the German COVID-19 research infrastructure. They comprise 2 subprojects: COVID-19 Data Exchange (CODEX) and Coordination on Mobile Pandemic Apps Best Practice and Solution Sharing (COMPASS). CODEX provides a centralized and secure data storage platform for research data, whereas in COMPASS, expert panels were gathered to develop a reference app framework for capturing patient-reported outcomes (PROs) that can be used by any researcher. Our study aims to integrate the data collected with the COMPASS reference app framework into the central CODEX platform, so that they can be used by secondary researchers. Although both projects used the Fast Healthcare Interoperability Resources (FHIR) standard, it was not used in a way that data could be shared directly. Given the short time frame and the parallel developments within the CODEX platform, a pragmatic and robust solution for an interface component was required. We have developed a means to facilitate and promote the use of the German Corona Consensus (GECCO) data set, a core data set for COVID-19 research in Germany. In this way, we ensured semantic interoperability for the app-collected PRO data with the COMPASS app. We also developed an interface component to sustain syntactic interoperability. The use of different FHIR types by the COMPASS reference app framework (the general-purpose FHIR Questionnaire) and the CODEX platform (eg, Patient, Condition, and Observation) was found to be the most significant obstacle. Therefore, we developed an interface component that realigns the Questionnaire items with the corresponding items in the GECCO data set and provides the correct resources for the CODEX platform. We extended the existing COMPASS questionnaire editor with an import function for GECCO items, which also tags them for the interface component. This ensures syntactic interoperability and eases the reuse of the GECCO data set for researchers. This paper shows how PRO data, which are collected across various studies conducted by different researchers, can be captured in a research-compatible way. This means that the data can be shared with a central research infrastructure and be reused by other researchers to gain more insights about COVID-19 and its sequelae.

Expression heterogeneity, tumor immune characteristics and the prognosis effects of OPRL1 in patients with tumors: a pan-cancer study combined with bioinformation analyses and in vitro validation

PurposeOpioids are currently the most frequently prescribed analgesics in clinical practice. However, their effect on cancer progression remains a topic of debate. Opioid receptors (ORs) are present in various types of tumor cells and their expression levels vary depending on the type of tumor. This study aims to explore and preliminarily characterize the association between four different ORs (μ, δ, κ, and nociception/orphanin FQ peptide receptor) and the prognosis of different types of tumors for comparison, with a focus on nociception/ orphanin FQ peptide receptor.MethodsThe expression levels of four ORs in normal tissues and immune cells were obtained from Human Protein Atlas (HPA) RNA-seq dataset, Monaco dataset, and Consensus dataset. Pan-cancer analysis was performed using the The Cancer Genome Atlas (TCGA) dataset, which included the expression of four ORs in different cancer types, significant copy-number alterations (sCNA), gene mutations of the four ORs, survival analysis, co-expression genes analysis, functional enrichment analyses, and correlations between ORs and immune cell infiltration levels. Based on the results of bioinformatic analysis, we selected 10 cancer cell lines for validation in vitro using specific agonists for the four ORs.ResultsOPRL1 (opioid related nociceptin receptor 1 gene) exhibited the highest abundance across different types of cancers, while OPRM1 (opioid receptor mu 1 gene) and OPRD1 (opioid receptor delta 1 gene) were barely detectable in multiple cancer types. Pan-cancer survival analysis revealed the overall worse/better prognosis of the four ORs in certain cancer types. Elevated levels of OPRM1 appear to be associated with poorer outcomes in breast invasive carcinoma and kidney renal clear cell carcinoma. Elevated OPRD1 levels are connected to worsen outcomes in kidney renal clear cell carcinoma and liver hepatocellular carcinoma, but better prognosis in bladder urothelial carcinoma. Increased OPRK1 (opioid receptor kappa 1 gene) expression is linked to a poorer prognosis in kidney renal papillary cell carcinoma. Furthermore, high OPRL1 expression relates to worse outcomes in bladder urothelial carcinoma and liver hepatocellular carcinoma, but better outcomes in breast invasive carcinoma and pancreatic adenocarcinoma. Functional enrichment analyses found that immune-related pathways were enriched in OPRK1 and OPRL1, with OPRL1 exhibiting the highest correlation with immune cell infiltration. Different effects on cell growth, migration, and invasion were observed in different cancer types upon the administration of agonists for the four ORs.ConclusionOPRL1 may play a vital role in monocytes and regulating the immune response and tumor-infiltrating macrophages. Due to its high abundance in different types of tumors, it may hold greater clinical significance for oncology patients. OPRK1 also participates in immune-related pathways. OPRL1 could potentially serve as therapeutic targets for different types of cancers.Graphical

An open source auto-segmentation algorithm for delineating heart and substructures – Development and validation within a multicenter lung cancer cohort

Background and purposeIrradiation of the heart in thoracic cancers raises toxicity concerns. For accurate dose estimation, automated heart and substructure segmentation is potentially useful. In this study, a hybrid automatic segmentation is developed. The accuracy of delineation and dose predictions were evaluated, testing the method's potential within heart toxicity studies. Materials and MethodsThe hybrid segmentation method delineated the heart, four chambers, three large vessels, and the coronary arteries. The method consisted of a nnU-net heart segmentation and partly atlas- and model-based segmentation of the substructures. The nnU-net training and atlas segmentation was based on lung cancer patients and was validated against a national consensus dataset of 12 patients with breast cancer. The accuracy of dose predictions between manual and auto-segmented heart and substructures was evaluated by transferring the dose distribution of 240 previously treated lung cancer patients to the consensus data set. ResultsThe hybrid auto-segmentation method performed well with a heart dice similarity coefficient (DSC) of 0.95, with no statistically significant difference between the automatic and manual delineations. The DSC for the chambers varied from 0.78-0.86 for the automatic segmentation and was comparable with the inter-observer variability. Most importantly, the automatic segmentation was as precise as the clinical experts in predicting the dose distribution to the heart and all substructures. ConclusionThe hybrid segmentation method performed well in delineating the heart and substructures. The prediction of dose by the automatic segmentation was aligned with the manual delineations, enabling measurement of heart and substructure dose in large cohorts. The delineation algorithm will be available for download.

Interoperable, Domain-Specific Extensions for the German Corona Consensus (GECCO) COVID-19 Research Data Set Using an Interdisciplinary, Consensus-Based Workflow: Data Set Development Study.

Background: The COVID-19 pandemic has spurred large-scale, interinstitutional research efforts. To enable these efforts, researchers must agree on data set definitions that not only cover all elements relevant to the respective medical specialty but also are syntactically and semantically interoperable. Therefore, the German Corona Consensus (GECCO) data set was developed as a harmonized, interoperable collection of the most relevant data elements for COVID-19-related patient research. As the GECCO data set is a compact core data set comprising data across all medical fields, the focused research within particular medical domains demands the definition of extension modules that include data elements that are the most relevant to the research performed in those individual medical specialties. Objective: We aimed to (1) specify a workflow for the development of interoperable data set definitions that involves close collaboration between medical experts and information scientists and (2) apply the workflow to develop data set definitions that include data elements that are the most relevant to COVID-19-related patient research regarding immunization, pediatrics, and cardiology. Methods: We developed a workflow to create data set definitions that were (1) content-wise as relevant as possible to a specific field of study and (2) universally usable across computer systems, institutions, and countries (ie, interoperable). We then gathered medical experts from 3 specialties-infectious diseases (with a focus on immunization), pediatrics, and cardiology-to select data elements that were the most relevant to COVID-19-related patient research in the respective specialty. We mapped the data elements to international standardized vocabularies and created data exchange specifications, using Health Level Seven International (HL7) Fast Healthcare Interoperability Resources (FHIR). All steps were performed in close interdisciplinary collaboration with medical domain experts and medical information specialists. Profiles and vocabulary mappings were syntactically and semantically validated in a 2-stage process. Results: We created GECCO extension modules for the immunization, pediatrics, and cardiology domains according to pandemic-related requests. The data elements included in each module were selected, according to the developed consensus-based workflow, by medical experts from these specialties to ensure that the contents aligned with their research needs. We defined data set specifications for 48 immunization, 150 pediatrics, and 52 cardiology data elements that complement the GECCO core data set. We created and published implementation guides, example implementations, and data set annotations for each extension module. Conclusions: The GECCO extension modules, which contain data elements that are the most relevant to COVID-19-related patient research on infectious diseases (with a focus on immunization), pediatrics, and cardiology, were defined in an interdisciplinary, iterative, consensus-based workflow that may serve as a blueprint for developing further data set definitions. The GECCO extension modules provide standardized and harmonized definitions of specialty-related data sets that can help enable interinstitutional and cross-country COVID-19 research in these specialties.

A.3 The Canadian registry for amyloidosis research: a national multi-disciplinary registry for real-world evidence

Background: The Canadian Registry for Amyloidosis Research (CRAR) is a nationwide disease registry of transthyretin (ATTR) and light-chain (AL) amyloidosis. Recent advances in disease-modifying therapy have improved prognosis, however there is a critical need for real-world evidence to address knowledge gaps, particularly longer-term therapeutic outcomes and surveillance strategies. Methods: A multi-stakeholder process was undertaken to develop a consensus dataset for ATTR- and AL-amyloidosis. This process included surveys to rank the importance of potential data items, and a consensus meeting of the CRAR steering committee, (comprised of multidisciplinary clinical experts, and patient organization representatives). Patients and patient organizations supported the development and implementation of a patient-reported dataset. Results: Consensus data items include disease onset, progression, severity, treatments, and outcomes, as well as patient-reported outcomes. Both prospective and retrospective (including deceased) patient cohorts are included. Further baseline data will be presented on an initial cohort of patients. Conclusions: CRAR has been established to collect a longitudinal, multidisciplinary dataset that will evaluate amyloidosis care and outcomes. CRAR has launched at multiple specialty amyloidosis centers nationally and is continually expanding. The growth of this program will promote opportunities to assess real-world safety and efficacy and inform the cost-effectiveness of therapies while supporting patient recruitment for research.

The Canadian Registry for Amyloidosis Research: A national multi-disciplinary registry for real-world evidence (P12-8.014)

The Canadian Registry for Amyloidosis Research: A national multi-disciplinary registry for real-world evidence (P12-8.014)

Development of a UK core dataset for geriatric medicine research: a position statement and results from a Delphi consensus process

BackgroundThere is lack of standardisation in assessment tools used in geriatric medicine research, which makes pooling of data and cross-study comparisons difficult.MethodsWe conducted a modified Delphi process to establish measures to be included within core and extended datasets for geriatric medicine research in the United Kingdom (UK). This included three complete questionnaire rounds, and one consensus meeting. Participants were selected from attendance at the NIHR Newcastle Biomedical Research Centre meeting, May 2019, and academic geriatric medicine e-mailing lists. Literature review was used to develop the initial questionnaire, with all responses then included in the second questionnaire. The third questionnaire used refined options from the second questionnaire with response ranking.ResultsNinety-eight responses were obtained across all questionnaire rounds (Initial: 19, Second: 21, Third: 58) from experienced and early career researchers in geriatric medicine. The initial questionnaire included 18 questions with short text responses, including one question for responders to suggest additional items. Twenty-six questions were included in the second questionnaire, with 108 within category options. The third questionnaire included three ranking, seven final agreement, and four binary option questions. Results were discussed at the consensus meeting. In our position statement, the final consensus dataset includes six core domains: demographics (age, gender, ethnicity, socioeconomic status), specified morbidities, functional ability (Barthel and/or Nottingham Extended Activities of Daily Living), Clinical Frailty Scale (CFS), cognition, and patient-reported outcome measures (dependent on research question). We also propose how additional variables should be measured within an extended dataset.ConclusionsOur core and extended datasets represent current consensus opinion of academic geriatric medicine clinicians across the UK. We consider the development and further use of these datasets will strengthen collaboration between researchers and academic institutions.

Interaction network of extracellular vesicles building universal analysis via eye tears: iNEBULA

Discovering the secrets of diseases from tear extracellular vesicles (EVs) is well-recognized and appreciated. However, a precise understanding of the interaction network between EV populations and their biogenesis from our body requires more in-depth and systematic analysis. Here, we report the biological profiles of different-size tear EV subsets from healthy individuals and the origins of EV proteins. We have identified about 1800 proteins and revealed the preferential differences in the biogenesis among distinct subsets. We observe that eye-related proteins that maintain retinal homeostasis and regulate inflammation are preferentially enriched in medium-size EVs (100 to 200 nm) fractions. Using universal analysis in combination with the Human Protein Atlas consensus dataset, we found the genesis of tear EV proteins with 37 tissues and 79 cell types. The proteins related to retinal neuronal cells, glial cells, and blood and immune cells are selectively enriched among EV subsets. Our studies in heterogeneous tear EVs provide building blocks for future transformative precision molecular diagnostics and therapeutics.

Consistency as a Data Quality Measure for German Corona Consensus Items Mapped from National Pandemic Cohort Network Data Collections.

As a national effort to better understand the current pandemic, three cohorts collect sociodemographic and clinical data from coronavirus disease 2019 (COVID-19) patients from different target populations within the German National Pandemic Cohort Network (NAPKON). Furthermore, the German Corona Consensus Dataset (GECCO) was introduced as a harmonized basic information model for COVID-19 patients in clinical routine. To compare the cohort data with other GECCO-based studies, data items are mapped to GECCO. As mapping from one information model to another is complex, an additional consistency evaluation of the mapped items is recommended to detect possible mapping issues or source data inconsistencies. The goal of this work is to assure high consistency of research data mapped to the GECCO data model. In particular, it aims at identifying contradictions within interdependent GECCO data items of the German national COVID-19 cohorts to allow investigation of possible reasons for identified contradictions. We furthermore aim at enabling other researchers to easily perform data quality evaluation on GECCO-based datasets and adapt to similar data models. All suitable data items from each of the three NAPKON cohorts are mapped to the GECCO items. A consistency assessment tool (dqGecco) is implemented, following the design of an existing quality assessment framework, retaining their-defined consistency taxonomies, including logical and empirical contradictions. Results of the assessment are verified independently on the primary data source. Our consistency assessment tool helped in correcting the mapping procedure and reveals remaining contradictory value combinations within COVID-19 symptoms, vital signs, and COVID-19 severity. Consistency rates differ between the different indicators and cohorts ranging from 95.84% up to 100%. An efficient and portable tool capable of discovering inconsistencies in the COVID-19 domain has been developed and applied to three different cohorts. As the GECCO dataset is employed in different platforms and studies, the tool can be directly applied there or adapted to similar information models.

MSS12 Presentation Time: 4:40 PM: A Proposed Formalism for Postoperative Dosimetry Evaluation for Permanently Implanted Collagen Tile Embedded Cs-131 Brachytherapy in the Brain

<h3>Purpose</h3> To propose a standardized formalism for delineating target volume contours and evaluating dose on postoperative imaging for patients receiving permanent low dose rate (LDR) brachytherapy (BT) in the brain. The BT applicator considered for this work consists of a 20 × 20 × 4 mm collagen tile embedded with four 3.5 U Cs-131 seeds with a 10 mm inter-seed spacing (GammaTile®, GT Medical Technologies, USA). Following maximum safe resection, multiple tiles are used to line the cavity and deliver a nominal dose of 60 Gy to a depth of 5 mm from the operative bed surface. The tiles are designed to limit the maximum dose to brain tissue via a 3 mm seed offset from brain surface that functions as an implanted internal compensator (Fig 1A). <h3>Materials and Methods</h3> Collagen tiles are placed by neurosurgeons under surgical visualization on resection the surfaces suspected to harbor subclinical disease; in this setting the Clinical Target Volume (CTV) is defined as the region of parenchyma adjacent to tile placement to a depth of 5 mm. As illustrated in Fig 1B-D, the algorithm proposed to generate a CTV is): 1) outline the brain, 2) outline all seeds using a Hounsfield Unit-based threshold tool, 3) expand the seed contour by 3 mm to approximate the tile/brain interface, 4) outline the cavity visualized on a registered MRI using the seeds+3 mm contour as a guide, 5) use a Boolean operation to obtain the intersection of the cavity contour expanded by 5 mm, the seed contour expanded by 0.94 mm, and the brain contour, and 5) subtract the cavity. Manual adjustments to ensure only areas with suspected subclinical disease may be required. Postoperative dose was calculated as per the AAPM TG43 3D formalism using the consensus dataset for the Cs-131 seed model utilized in the GammaTile device. Dose statistics pertaining to the CTV, including the mean isodose values covering 90% and 95% of the volume (D90% and D95%), the volume receiving at least 90% and 95% of the prescription 60 Gy (V90% and V95%), minimum dose, and maximum dose were tabulated for 11 patients using a commercial Treatment Planning System (MIM, MIM Software, USA). <h3>Results</h3> Volumes (cc) of cavity contours across all patients ranged from 3.3 to 49.9 and the CTVs ranged from 6.9 to 27.1. Mean D90% and D95% (Gy) to the CTV was 55.8 (45.5 - 73.8) and 49.4 (36.3 - 67.1), respectively. Mean V90% and V95% (% of total volume) to the CTV was 88.7 (77.6 - 99.8) and 85.5 (72.6 - 99.4), respectively. Mean minimum and maximum doses (Gy) to the CTV were 36.3 (19. - 64.7) and 426.3 Gy (265.6 - 573.9), respectively. Note, the maximum dose to the <i>cavity</i> is not accurately calculated by the TPS but is estimated to be in the range of 800 - 1500 Gy, indicating the tiles reduce maximum dose to brain by ∼28-53%. <h3>Conclusions</h3> A formalism to 1) generate a CTV representing brain containing subclinical disease following gross tumor resection and collagen tile BT implant and 2) obtain dose statistics is proposed. The contouring algorithm utilizes the surgical cavity (well-seen on MRI), LDR seeds (placed with direct surgical guidance, and well-seen on CT) and Boolean operations, resulting in a straightforward and reproducible CTV. The proposed dose statistics (D90%, D95%, V90%, V95%, min, and max) proved to be useful metrics for dose review. The standardized formalism for contouring CTV and its use in dose evaluation will be further tested to determine its utility in minimizing interobserver variability. Once fully validated, this formalism will be used to generate recommendations for quantitative evaluation GammaTile implant dosimetry.

The Architecture of a Feasibility Query Portal for Distributed COVID-19 Fast Healthcare Interoperability Resources (FHIR) Patient Data Repositories: Design and Implementation Study.

BackgroundAn essential step in any medical research project after identifying the research question is to determine if there are sufficient patients available for a study and where to find them. Pursuing digital feasibility queries on available patient data registries has proven to be an excellent way of reusing existing real-world data sources. To support multicentric research, these feasibility queries should be designed and implemented to run across multiple sites and securely access local data. Working across hospitals usually involves working with different data formats and vocabularies. Recently, the Fast Healthcare Interoperability Resources (FHIR) standard was developed by Health Level Seven to address this concern and describe patient data in a standardized format. The Medical Informatics Initiative in Germany has committed to this standard and created data integration centers, which convert existing data into the FHIR format at each hospital. This partially solves the interoperability problem; however, a distributed feasibility query platform for the FHIR standard is still missing.ObjectiveThis study described the design and implementation of the components involved in creating a cross-hospital feasibility query platform for researchers based on FHIR resources. This effort was part of a large COVID-19 data exchange platform and was designed to be scalable for a broad range of patient data.MethodsWe analyzed and designed the abstract components necessary for a distributed feasibility query. This included a user interface for creating the query, backend with an ontology and terminology service, middleware for query distribution, and FHIR feasibility query execution service.ResultsWe implemented the components described in the Methods section. The resulting solution was distributed to 33 German university hospitals. The functionality of the comprehensive network infrastructure was demonstrated using a test data set based on the German Corona Consensus Data Set. A performance test using specifically created synthetic data revealed the applicability of our solution to data sets containing millions of FHIR resources. The solution can be easily deployed across hospitals and supports feasibility queries, combining multiple inclusion and exclusion criteria using standard Health Level Seven query languages such as Clinical Quality Language and FHIR Search. Developing a platform based on multiple microservices allowed us to create an extendable platform and support multiple Health Level Seven query languages and middleware components to allow integration with future directions of the Medical Informatics Initiative.ConclusionsWe designed and implemented a feasibility platform for distributed feasibility queries, which works directly on FHIR-formatted data and distributed it across 33 university hospitals in Germany. We showed that developing a feasibility platform directly on the FHIR standard is feasible.

CrowdGO: Machine learning and semantic similarity guided consensus Gene Ontology annotation.

Characterising gene function for the ever-increasing number and diversity of species with annotated genomes relies almost entirely on computational prediction methods. These software are also numerous and diverse, each with different strengths and weaknesses as revealed through community benchmarking efforts. Meta-predictors that assess consensus and conflict from individual algorithms should deliver enhanced functional annotations. To exploit the benefits of meta-approaches, we developed CrowdGO, an open-source consensus-based Gene Ontology (GO) term meta-predictor that employs machine learning models with GO term semantic similarities and information contents. By re-evaluating each gene-term annotation, a consensus dataset is produced with high-scoring confident annotations and low-scoring rejected annotations. Applying CrowdGO to results from a deep learning-based, a sequence similarity-based, and two protein domain-based methods, delivers consensus annotations with improved precision and recall. Furthermore, using standard evaluation measures CrowdGO performance matches that of the community's best performing individual methods. CrowdGO therefore offers a model-informed approach to leverage strengths of individual predictors and produce comprehensive and accurate gene functional annotations.

Generation of a Fast Healthcare Interoperability Resources (FHIR)-based Ontology for Federated Feasibility Queries in the Context of COVID-19: Feasibility Study

BackgroundThe COVID-19 pandemic highlighted the importance of making research data from all German hospitals available to scientists to respond to current and future pandemics promptly. The heterogeneous data originating from proprietary systems at hospitals' sites must be harmonized and accessible. The German Corona Consensus Dataset (GECCO) specifies how data for COVID-19 patients will be standardized in Fast Healthcare Interoperability Resources (FHIR) profiles across German hospitals. However, given the complexity of the FHIR standard, the data harmonization is not sufficient to make the data accessible. A simplified visual representation is needed to reduce the technical burden, while allowing feasibility queries.ObjectiveThis study investigates how a search ontology can be automatically generated using FHIR profiles and a terminology server. Furthermore, it describes how this ontology can be used in a user interface (UI) and how a mapping and a terminology tree created together with the ontology can translate user input into FHIR queries.MethodsWe used the FHIR profiles from the GECCO data set combined with a terminology server to generate an ontology and the required mapping files for the translation. We analyzed the profiles and identified search criteria for the visual representation. In this process, we reduced the complex profiles to code value pairs for improved usability. We enriched our ontology with the necessary information to display it in a UI. We also developed an intermediate query language to transform the queries from the UI to federated FHIR requests. Separation of concerns resulted in discrepancies between the criteria used in the intermediate query format and the target query language. Therefore, a mapping was created to reintroduce all information relevant for creating the query in its target language. Further, we generated a tree representation of the ontology hierarchy, which allows resolving child concepts in the process.ResultsIn the scope of this project, 82 (99%) of 83 elements defined in the GECCO profile were successfully implemented. We verified our solution based on an independently developed test patient. A discrepancy between the test data and the criteria was found in 6 cases due to different versions used to generate the test data and the UI profiles, the support for specific code systems, and the evaluation of postcoordinated Systematized Nomenclature of Medicine (SNOMED) codes. Our results highlight the need for governance mechanisms for version changes, concept mapping between values from different code systems encoding the same concept, and support for different unit dimensions.ConclusionsWe developed an automatic process to generate ontology and mapping files for FHIR-formatted data. Our tests found that this process works for most of our chosen FHIR profile criteria. The process established here works directly with FHIR profiles and a terminology server, making it extendable to other FHIR profiles and demonstrating that automatic ontology generation on FHIR profiles is feasible.

A Consensus Compound/Bioactivity Dataset for Data-Driven Drug Design and Chemogenomics.

Publicly available compound and bioactivity databases provide an essential basis for data-driven applications in life-science research and drug design. By analyzing several bioactivity repositories, we discovered differences in compound and target coverage advocating the combined use of data from multiple sources. Using data from ChEMBL, PubChem, IUPHAR/BPS, BindingDB, and Probes & Drugs, we assembled a consensus dataset focusing on small molecules with bioactivity on human macromolecular targets. This allowed an improved coverage of compound space and targets, and an automated comparison and curation of structural and bioactivity data to reveal potentially erroneous entries and increase confidence. The consensus dataset comprised of more than 1.1 million compounds with over 10.9 million bioactivity data points with annotations on assay type and bioactivity confidence, providing a useful ensemble for computational applications in drug design and chemogenomics.

Supervised promoter recognition: a benchmark framework

MotivationDeep learning has become a prevalent method in identifying genomic regulatory sequences such as promoters. In a number of recent papers, the performance of deep learning models has continually been reported as an improvement over alternatives for sequence-based promoter recognition. However, the performance improvements in these models do not account for the different datasets that models are evaluated on. The lack of a consensus dataset and procedure for benchmarking purposes has made the comparison of each model’s true performance difficult to assess.ResultsWe present a framework called Supervised Promoter Recognition Framework (‘SUPR REF’) capable of streamlining the complete process of training, validating, testing, and comparing promoter recognition models in a systematic manner. SUPR REF includes the creation of biologically relevant benchmark datasets to be used in the evaluation process of deep learning promoter recognition models. We showcase this framework by comparing the models’ performances on alternative datasets, and properly evaluate previously published models on new benchmark datasets. Our results show that the reliability of deep learning ab initio promoter recognition models on eukaryotic genomic sequences is still not at a sufficient level, as overall performance is still low. These results originate from a subset of promoters, the well-known RNA Polymerase II core promoters. Furthermore, given the observational nature of these data, cross-validation results from small promoter datasets need to be interpreted with caution.

Cell types of origin of the cell-free transcriptome

Cell-free RNA from liquid biopsies can be analyzed to determine disease tissue of origin. We extend this concept to identify cell types of origin using the Tabula Sapiens transcriptomic cell atlas as well as individual tissue transcriptomic cell atlases in combination with the Human Protein Atlas RNA consensus dataset. We define cell type signature scores, which allow the inference of cell types that contribute to cell-free RNA for a variety of diseases.