The Human Glycome Atlas Project for cataloging all glycan-related omics data in human.

The Human Glycome Atlas (HGA) Project was launched in April 2023, spearheaded by three Japanese institutes: the Tokai National Higher Education and Research System, the National Institutes of Natural Sciences, and Soka University. This was the first time that a field in the life sciences was adopted by the Ministry of Education, Culture, Sports, Science and Technology (MEXT) for a Large-scale Academic Frontiers Promotion Project. This project aims to construct a knowledgebase of human glycans and glycoproteins as a standard for the human glycome. A high-throughput pipeline for comprehensively analyzing 20,000 blood samples in its first five years is planned, at which time an access-controlled version of a human glycomics knowledgebase, called TOHSA, will be released. By the end of the final tenth year, TOHSA will provide a central resource linking human glycan data with other omics data including disease-related information.

Virus-CKB: an integrated bioinformatics platform and analysis resource for COVID-19 research.

Given the scale and rapid spread of the coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), there is an urgent need for medicines that can help before vaccines are available. In this study, we present a viral-associated disease-specific chemogenomics knowledgebase (Virus-CKB) and apply our computational systems pharmacology-target mapping to rapidly predict the FDA-approved drugs which can quickly progress into clinical trials to meet the urgent demand of the COVID-19 outbreak. Virus-CKB reuses the underlying platform of our DAKB-GPCRs but adds new features like multiple-compound support, multi-cavity protein support and customizable symbol display. Our one-stop computing platform describes the chemical molecules, genes and proteins involved in viral-associated diseases regulation. To date, Virus-CKB archived 65 antiviral drugs in the market, 107 viral-related targets with 189 available 3D crystal or cryo-EM structures and 2698 chemical agents reported for these target proteins. Moreover, Virus-CKB is implemented with web applications for the prediction of the relevant protein targets and analysis and visualization of the outputs, including HTDocking, TargetHunter, BBB predictor, NGL Viewer, Spider Plot, etc. The Virus-CKB server is accessible at https://www.cbligand.org/g/virus-ckb.

Tri©DB: an integrated platform of knowledgebase and reporting system for cancer precision medicine.

BACKGROUND: With the development of cancer precision medicine, a huge amount of high-dimensional cancer information has rapidly accumulated regarding gene alterations, diseases, therapeutic interventions and various annotations. The information is highly fragmented across multiple different sources, making it highly challenging to effectively utilize and exchange the information. Therefore, it is essential to create a resource platform containing well-aggregated, carefully mined, and easily accessible data for effective knowledge sharing. METHODS: In this study, we have developed "Consensus Cancer Core" (Tri©DB), a new integrative cancer precision medicine knowledgebase and reporting system by mining and harmonizing multifaceted cancer data sources, and presenting them in a centralized platform with enhanced functionalities for accessibility, annotation and analysis. RESULTS: The knowledgebase provides the currently most comprehensive information on cancer precision medicine covering more than 40 annotation entities, many of which are novel and have never been explored previously. Tri©DB offers several unique features: (i) harmonizing the cancer-related information from more than 30 data sources into one integrative platform for easy access; (ii) utilizing a variety of data analysis and graphical tools for enhanced user interaction with the high-dimensional data; (iii) containing a newly developed reporting system for automated annotation and therapy matching for external patient genomic data. Benchmark test indicated that Tri©DB is able to annotate 46% more treatments than two officially recognized resources, oncoKB and MCG. Tri©DB was further shown to have achieved 94.9% concordance with administered treatments in a real clinical trial. CONCLUSIONS: The novel features and rich functionalities of the new platform will facilitate full access to cancer precision medicine data in one single platform and accommodate the needs of a broad range of researchers not only in translational medicine, but also in basic biomedical research. We believe that it will help to promote knowledge sharing in cancer precision medicine. Tri©DB is freely available at www.biomeddb.org , and is hosted on a cutting-edge technology architecture supporting all major browsers and mobile handsets.

Creating an ignorance-base: Exploring known unknowns in the scientific literature.

BACKGROUND: Scientific discovery progresses by exploring new and uncharted territory. More specifically, it advances by a process of transforming unknown unknowns first into known unknowns, and then into knowns. Over the last few decades, researchers have developed many knowledge bases to capture and connect the knowns, which has enabled topic exploration and contextualization of experimental results. But recognizing the unknowns is also critical for finding the most pertinent questions and their answers. Prior work on known unknowns has sought to understand them, annotate them, and automate their identification. However, no knowledge-bases yet exist to capture these unknowns, and little work has focused on how scientists might use them to trace a given topic or experimental result in search of open questions and new avenues for exploration. We show here that a knowledge base of unknowns can be connected to ontologically grounded biomedical knowledge to accelerate research in the field of prenatal nutrition. RESULTS: We present the first ignorance-base, a knowledge-base created by combining classifiers to recognize ignorance statements (statements of missing or incomplete knowledge that imply a goal for knowledge) and biomedical concepts over the prenatal nutrition literature. This knowledge-base places biomedical concepts mentioned in the literature in context with the ignorance statements authors have made about them. Using our system, researchers interested in the topic of vitamin D and prenatal health were able to uncover three new avenues for exploration (immune system, respiratory system, and brain development) by searching for concepts enriched in ignorance statements. These were buried among the many standard enriched concepts. Additionally, we used the ignorance-base to enrich concepts connected to a gene list associated with vitamin D and spontaneous preterm birth and found an emerging topic of study (brain development) in an implied field (neuroscience). The researchers could look to the field of neuroscience for potential answers to the ignorance statements. CONCLUSION: Our goal is to help students, researchers, funders, and publishers better understand the state of our collective scientific ignorance (known unknowns) in order to help accelerate research through the continued illumination of and focus on the known unknowns and their respective goals for scientific knowledge.

"KRiShI": a manually curated knowledgebase on rice sheath blight disease.

Knowledgebase for rice sheath blight information (KRiShI) is a manually curated user-friendly knowledgebase for rice sheath blight (SB) disease that allows users to efficiently mine, visualize, search, benchmark, download, and update meaningful data and information related to SB using its easy and interactive interface. KRiShI collects and integrates widely scattered and unstructured information from various scientific literatures, stores it under a single window, and makes it available to the community in a user-friendly manner. From basic information, best management practices, host resistance, differentially expressed genes, proteins, metabolites, resistance genes, pathways, and OMICS scale experiments, KRiShI presents these in the form of easy and comprehensive tables, diagrams, and pictures. The "Search" tab allows users to verify if their input rice gene id(s) are Rhizoctonia solani (R. solani) responsive and/or resistant. KRiShI will serve as a valuable resource for easy and quick access to data and information related to rice SB disease for both the researchers and the farmers. To encourage community curation a submission facility is made available. KRiShI can be found at http://www.tezu.ernet.in/krishi .

Towards more personalized digital health interventions: a clustering method of action and coping plans to promote physical activity.

BACKGROUND: Despite effectiveness of action and coping planning in digital health interventions to promote physical activity (PA), attrition rates remain high. Indeed, support to make plans is often abstract and similar for each individual. Nevertheless, people are different, and context varies. Tailored support at the content level, involving suggestions of specific plans that are personalized to the individual, may reduce attrition and improve outcomes in digital health interventions. The aim of this study was to investigate whether user information relates toward specific action and coping plans using a clustering method. In doing so, we demonstrate how knowledge can be acquired in order to develop a knowledge-base, which might provide personalized suggestions in a later phase. METHODS: To establish proof-of-concept for this approach, data of 65 healthy adults, including 222 action plans and 204 coping plans, were used and were collected as part of the digital health intervention MyPlan 2.0 to promote PA. As a first step, clusters of action plans, clusters of coping plans and clusters of combinations of action plans and barriers of coping plans were identified using hierarchical clustering. As a second step, relations with user information (i.e. gender, motivational stage, ...) were examined using anova's and chi2-tests. RESULTS: First, three clusters of action plans, eight clusters of coping plans and eight clusters of the combination of action and coping plans were identified. Second, relating these clusters to user information was possible for action plans: 1) Users with a higher BMI related more to outdoor leisure activities (F = 13.40, P < .001), 2) Women, users that didn't perform PA regularly yet, or users with a job related more to household activities (X2 = 16.92, P < .001; X2 = 20.34, P < .001; X2 = 10.79, P = .004; respectively), 3) Younger users related more to active transport and different sports activities (F = 14.40, P < .001). However, relating clusters to user information proved difficult for the coping plans and combination of action and coping plans. CONCLUSIONS: The approach used in this study might be a feasible approach to acquire input for a knowledge-base, however more data (i.e. contextual and dynamic user information) from possible end users should be acquired in future research. This might result in a first type of context-aware personalized suggestions on the content level. TRIAL REGISTRATION: The digital health intervention MyPlan 2.0 was preregistered as a clinical trial (ID:NCT03274271). Release date: 6-September-2017.

LinkedImm: a linked data graph database for integrating immunological data.

BACKGROUND: Many systems biology studies leverage the integration of multiple data types (across different data sources) to offer a more comprehensive view of the biological system being studied. While SQL (Structured Query Language) databases are popular in the biomedical domain, NoSQL database technologies have been used as a more relationship-based, flexible and scalable method of data integration. RESULTS: We have created a graph database integrating data from multiple sources. In addition to using a graph-based query language (Cypher) for data retrieval, we have developed a web-based dashboard that allows users to easily browse and plot data without the need to learn Cypher. We have also implemented a visual graph query interface for users to browse graph data. Finally, we have built a prototype to allow the user to query the graph database in natural language. CONCLUSION: We have demonstrated the feasibility and flexibility of using a graph database for storing and querying immunological data with complex biological relationships. Querying a graph database through such relationships has the potential to discover novel relationships among heterogeneous biological data and metadata.

An Update on MRMAssayDB: A Comprehensive Resource for Targeted Proteomics Assays in the Community.

Precise multiplexed quantification of proteins in biological samples can be achieved by targeted proteomics using multiple or parallel reaction monitoring (MRM/PRM). Combined with internal standards, the method achieves very good repeatability and reproducibility enabling excellent protein quantification and allowing longitudinal and cohort studies. A laborious part of performing such experiments lies in the preparation steps dedicated to the development and validation of individual protein assays. Several public repositories host information on targeted proteomics assays, including NCI's Clinical Proteomic Tumor Analysis Consortium assay portals, PeptideAtlas SRM Experiment Library, SRMAtlas, PanoramaWeb, and PeptideTracker, with all offering varying levels of details. We introduced MRMAssayDB in 2018 as an integrated resource for targeted proteomics assays. The Web-based application maps and links the assays from the repositories, includes comprehensive up-to-date protein and sequence annotations, and provides multiple visualization options on the peptide and protein level. We have extended MRMAssayDB with more assays and extensive annotations. Currently it contains >828 000 assays covering >51 000 proteins from 94 organisms, of which >17 000 proteins are present in >2400 biological pathways, and >48 000 mapping to >21 000 Gene Ontology terms. This is an increase of about four times the number of assays since introduction. We have expanded annotations of interaction, biological pathways, and disease associations. A newly added visualization module for coupled molecular structural annotation browsing allows the user to interactively examine peptide sequence and any known PTMs and disease mutations, and map all to available protein 3D structures. Because of its integrative approach, MRMAssayDB enables a holistic view of suitable proteotypic peptides and commonly used transitions in empirical data. Availability: http://mrmassaydb.proteincentre.com.

An Efficient 3D Human Pose Retrieval and Reconstruction from 2D Image-Based Landmarks.

We propose an efficient and novel architecture for 3D articulated human pose retrieval and reconstruction from 2D landmarks extracted from a 2D synthetic image, an annotated 2D image, an in-the-wild real RGB image or even a hand-drawn sketch. Given 2D joint positions in a single image, we devise a data-driven framework to infer the corresponding 3D human pose. To this end, we first normalize 3D human poses from Motion Capture (MoCap) dataset by eliminating translation, orientation, and the skeleton size discrepancies from the poses and then build a knowledge-base by projecting a subset of joints of the normalized 3D poses onto 2D image-planes by fully exploiting a variety of virtual cameras. With this approach, we not only transform 3D pose space to the normalized 2D pose space but also resolve the 2D-3D cross-domain retrieval task efficiently. The proposed architecture searches for poses from a MoCap dataset that are near to a given 2D query pose in a definite feature space made up of specific joint sets. These retrieved poses are then used to construct a weak perspective camera and a final 3D posture under the camera model that minimizes the reconstruction error. To estimate unknown camera parameters, we introduce a nonlinear, two-fold method. We exploit the retrieved similar poses and the viewing directions at which the MoCap dataset was sampled to minimize the projection error. Finally, we evaluate our approach thoroughly on a large number of heterogeneous 2D examples generated synthetically, 2D images with ground-truth, a variety of real in-the-wild internet images, and a proof of concept using 2D hand-drawn sketches of human poses. We conduct a pool of experiments to perform a quantitative study on PARSE dataset. We also show that the proposed system yields competitive, convincing results in comparison to other state-of-the-art methods.

The Role of Knowledge Availability in Forming Inferences with Rural Middle Grade English Learners.

We investigated differences in knowledge-based inferencing between rural, middle grade monolingual English-speaking students and English learners. Students were introduced to facts about an imaginary planet Gan followed by a multi-episode story about Gan. Participants were tested on the accuracy of fact recall and inferences using this knowledge at three time points (i.e., immediate, one-week, and one-month follow-up). Results show that monolingual English-speaking students significantly outperformed English learners on the inference task. Both subgroups made elaborative inferences more accurately than coherence. Students' ability to recall knowledge base facts was the strongest predictor of their ability to accurately make inferences using this knowledge at each time point.

Basket Studies: Redefining Clinical Trials in the Era of Genome-Driven Oncology.

Understanding a tumor's detailed molecular profile has become increasingly necessary to deliver the standard of care for patients with advanced cancer. Innovations in both tumor genomic sequencing technology and the development of drugs that target molecular alterations have fueled recent gains in genome-driven oncology care. "Basket studies," or histology-agnostic clinical trials in genomically selected patients, represent one important research tool to continue making progress in this field. We review key aspects of genome-driven oncology care, including the purpose and utility of basket studies, biostatistical considerations in trial design, genomic knowledgebase development, and patient matching and enrollment models, which are critical for translating our genomic knowledge into clinically meaningful outcomes.

OncoPDSS: an evidence-based clinical decision support system for oncology pharmacotherapy at the individual level.

BACKGROUND: Precision oncology pharmacotherapy relies on precise patient-specific alterations that impact drug responses. Due to rapid advances in clinical tumor sequencing, an urgent need exists for a clinical support tool that automatically interprets sequencing results based on a structured knowledge base of alteration events associated with clinical implications. RESULTS: Here, we introduced the Oncology Pharmacotherapy Decision Support System (OncoPDSS), a web server that systematically annotates the effects of alterations on drug responses. The platform integrates actionable evidence from several well-known resources, distills drug indications from anti-cancer drug labels, and extracts cancer clinical trial data from the ClinicalTrials.gov database. A therapy-centric classification strategy was used to identify potentially effective and non-effective pharmacotherapies from user-uploaded alterations of multi-omics based on integrative evidence. For each potentially effective therapy, clinical trials with faculty information were listed to help patients and their health care providers find the most suitable one. CONCLUSIONS: OncoPDSS can serve as both an integrative knowledge base on cancer precision medicine, as well as a clinical decision support system for cancer researchers and clinical oncologists. It receives multi-omics alterations as input and interprets them into pharmacotherapy-centered information, thus helping clinicians to make clinical pharmacotherapy decisions. The OncoPDSS web server is freely accessible at https://oncopdss.capitalbiobigdata.com .

User-centered design of a web-based crowdsourcing-integrated semantic text annotation tool for building a mental health knowledge base.

BACKGROUND: One in five U.S. adults lives with some kind of mental health condition and 4.6% of all U.S. adults have a serious mental illness. The Internet has become the first place for these people to seek online mental health information for help. However, online mental health information is not well-organized and often of low quality. There have been efforts in building evidence-based mental health knowledgebases curated with information manually extracted from the high-quality scientific literature. Manual extraction is inefficient. Crowdsourcing can potentially be a low-cost mechanism to collect labeled data from non-expert laypeople. However, there is not an existing annotation tool integrated with popular crowdsourcing platforms to perform the information extraction tasks. In our previous work, we prototyped a Semantic Text Annotation Tool (STAT) to address this gap. OBJECTIVE: We aimed to refine the STAT prototype (1) to improve its usability and (2) to enhance the crowdsourcing workflow efficiency to facilitate the construction of evidence-based mental health knowledgebase, following a user-centered design (UCD) approach. METHODS: Following UCD principles, we conducted four design iterations to improve the initial STAT prototype. In the first two iterations, usability testing focus groups were conducted internally with 8 participants recruited from a convenient sample, and the usability was evaluated with a modified System Usability Scale (SUS). In the following two iterations, usability testing was conducted externally using the Amazon Mechanical Turk (MTurk) platform. In each iteration, we summarized the usability testing results through thematic analysis, identified usability issues, and conducted a heuristic evaluation to map identified usability issues to Jakob Nielsen's usability heuristics. We collected suggested improvements in the usability testing sessions and enhanced STAT accordingly in the next UCD iteration. After four UCD iterations, we conducted a case study of the system on MTurk using mental health related scientific literature. We compared the performance of crowdsourcing workers with two expert annotators from two aspects: efficiency and quality. RESULTS: The SUS score increased from 70.3 ± 12.5 to 81.1 ± 9.8 after the two internal UCD iterations as we improved STAT's functionality based on the suggested improvements. We then evaluated STAT externally through MTurk in the following two iterations. The SUS score decreased to 55.7 ± 20.1 in the third iteration, probably because of the complexity of the tasks. After further simplification of STAT and the annotation tasks with an improved annotation guideline, the SUS score increased to 73.8 ± 13.8 in the fourth iteration of UCD. In the evaluation case study, on average, the workers spent 125.5 ± 69.2 s on the onboarding tutorial and the crowdsourcing workers spent significantly less time on the annotation tasks compared to the two experts. In terms of annotation quality, the workers' annotation results achieved average F1-scores ranged from 0.62 to 0.84 for the different sentences. CONCLUSIONS: We successfully developed a web-based semantic text annotation tool, STAT, to facilitate the curation of semantic web knowledgebases through four UCD iterations. The lessons learned from the UCD process could serve as a guide to further enhance STAT and the development and design of other crowdsourcing-based semantic text annotation tasks. Our study also showed that a well-organized, informative annotation guideline is as important as the annotation tool itself. Further, we learned that a crowdsourcing task should consist of multiple simple microtasks rather than a complicated task.

Insight in Adhesion Protein Sialylation and Microgravity Dependent Cell Adhesion-An Omics Network Approach.

The adhesion behavior of human tissue cells changes in vitro, when gravity forces affecting these cells are modified. To understand the mechanisms underlying these changes, proteins involved in cell-cell or cell-extracellular matrix adhesion, their expression, accumulation, localization, and posttranslational modification (PTM) regarding changes during exposure to microgravity were investigated. As the sialylation of adhesion proteins is influencing cell adhesion on Earth in vitro and in vivo, we analyzed the sialylation of cell adhesion molecules detected by omics studies on cells, which change their adhesion behavior when exposed to microgravity. Using a knowledge graph created from experimental omics data and semantic searches across several reference databases, we studied the sialylation of adhesion proteins glycosylated at their extracellular domains with regards to its sensitivity to microgravity. This way, experimental omics data networked with the current knowledge about the binding of sialic acids to cell adhesion proteins, its regulation, and interactions in between those proteins provided insights into the mechanisms behind our experimental findings, suggesting that balancing the sialylation against the de-sialylation of the terminal ends of the adhesion proteins' glycans influences their binding activity. This sheds light on the transition from two- to three-dimensional growth observed in microgravity, mirroring cell migration and cancer metastasis in vivo.

Augmenting cancer cell proteomics with cellular images - A semantic approach to understand focal adhesion.

If monolayers of cancer cells are exposed to microgravity, some of the cells cease adhering to the bottom of a culture flask and join three-dimensional aggregates floating in the culture medium. Searching reasons for this change in phenotype, we performed proteome analyses and learnt that accumulation and posttranslational modification of proteins involved in cell-matrix and cell-cell adhesion are affected. To further investigate these proteins, we developed a methodology to find histological images about focal adhesion complex (FA) proteins. Selecting proteins expressed by human FTC-133 and MCF-7 cancer cells and known to be incorporated in FA, we transformed the experimental data to RDF to establish a core semantic knowledgebase. Applying iterative SPARQL queries to Linked Open Databases, we augmented these data with additional functional, transformation- and aggregation-related relationships. Using reasoning, we retrieved publications with images about the spatial arrangement of proteins incorporated in FA. Contextualizing those images enabled us to gain insights about FA of cells changing their site of growth, and to independently validate our experimental results. This new way to link experimental proteome data to biomedical knowledge from various sources via searching images may generally be applied in science when images are a tool of knowledge dissemination.

DES-ncRNA: A knowledgebase for exploring information about human micro and long noncoding RNAs based on literature-mining.

Noncoding RNAs (ncRNAs), particularly microRNAs (miRNAs) and long ncRNAs (lncRNAs), are important players in diseases and emerge as novel drug targets. Thus, unraveling the relationships between ncRNAs and other biomedical entities in cells are critical for better understanding ncRNA roles that may eventually help develop their use in medicine. To support ncRNA research and facilitate retrieval of relevant information regarding miRNAs and lncRNAs from the plethora of published ncRNA-related research, we developed DES-ncRNA ( www.cbrc.kaust.edu.sa/des_ncrna ). DES-ncRNA is a knowledgebase containing text- and data-mined information from public scientific literature and other public resources. Exploration of mined information is enabled through terms and pairs of terms from 19 topic-specific dictionaries including, for example, antibiotics, toxins, drugs, enzymes, mutations, pathways, human genes and proteins, drug indications and side effects, mutations, diseases, etc. DES-ncRNA contains approximately 878,000 associations of terms from these dictionaries of which 36,222 (5,373) are with regards to miRNAs (lncRNAs). We provide several ways to explore information regarding ncRNAs to users including controlled generation of association networks as well as hypotheses generation. We show an example how DES-ncRNA can aid research on Alzheimer disease and suggest potential therapeutic role for Fasudil. DES-ncRNA is a powerful tool that can be used on its own or as a complement to the existing resources, to support research in human ncRNA. To our knowledge, this is the only knowledgebase dedicated to human miRNAs and lncRNAs derived primarily through literature-mining enabling exploration of a broad spectrum of associated biomedical entities, not paralleled by any other resource.

Semantic analysis of thyroid cancer cell proteins obtained from rare research opportunities.

Research in natural sciences is mainly done by means of experiments. Some of those experiments such as spaceflight-dependent experiments are extremely laborious, complex and expensive. Hence, they often remain rare events with little chances of statistical tests and possibilities of repetition. In order to make each single event as valuable as possible, a sophisticated comparison of experimental data received with the hundreds of millions of computer-stored documents appears necessary. We used results of an earlier study on proteome analysis of microgravity-exposed human thyroid cancer cells, selected twenty proteins which appeared gravity sensitive and investigated whether their change observed in cells under the loss of gravity could cause health problems in astronauts. Using network analysis via Knowledge Explorer (KE) we searched the literature for diseases related to one or more of the selected proteins. After using Linked Open Data (LOD) and other public resources to establish a comprehensive semantic knowledgebase around functional properties of the selected proteins, the collection's network was used to query a set of databases for the proteins' involvement in biosystems and human diseases. Finally, possible countermeasures could be proposed.

A knowledgebase of the human Alu repetitive elements.

Alu elements are the most abundant retrotransposons in the human genome with more than one million copies. Alu repeats have been reported to participate in multiple processes related with genome regulation and compartmentalization. Moreover, they have been involved in the facilitation of pathological mutations in many diseases, including cancer. The contribution of Alus and other repeats in genomic regulation is often overlooked because their study poses technical and analytical challenges hardly attainable with conventional strategies. Here we propose the integration of ontology-based semantic methods to query a knowledgebase for the human Alus. The knowledgebase for the human Alus leverages Sequence (SO) and Gene Ontologies (GO) and is devoted to address functional and genetic information in the genomic context of the Alus. For each Alu element, the closest gene and transcript are stored, as well their functional annotation according to GO, the state of the chromatin and the transcription factors binding sites inside the Alu. The model uses Web Ontology Language (OWL) and Semantic Web Rule Language (SWRL). As a case of use and to illustrate the utility of the tool, we have evaluated the epigenetic states of Alu repeats associated with gene promoters according to their transcriptional activity. The ontology is easily extendable, offering a scaffold for the inclusion of new experimental data. The RDF/XML formalization is freely available at http://aluontology.sourceforge.net/.

A multi-technique approach to bridge electronic case report form design and data standard adoption.

BACKGROUND AND OBJECTIVE: The importance of data standards when integrating clinical research data has been recognized. The common data element (CDE) is a consensus-based data element for data harmonization and sharing between clinical researchers, it can support data standards adoption and mapping. However, the lack of a suitable methodology has become a barrier to data standard adoption. Our aim was to demonstrate an approach that allowed clinical researchers to design electronic case report forms (eCRFs) that complied with the data standard. METHODS: We used a multi-technique approach, including information retrieval, natural language processing and an ontology-based knowledgebase to facilitate data standard adoption using the eCRF design. The approach took research questions as query texts with the aim of retrieving and associating relevant CDEs with the research questions. RESULTS: The approach was implemented using a CDE-based eCRF builder, which was evaluated using CDE- related questions from CRFs used in the Parkinson Disease Biomarker Program, as well as CDE-unrelated questions from a technique support website. Our approach had a precision of 0.84, a recall of 0.80, a F-measure of 0.82 and an error of 0.31. Using the 303 testing CDE-related questions, our approach responded and provided suggested CDEs for 88.8% (269/303) of the study questions with a 90.3% accuracy (243/269). The reason for any missed and failed responses was also analyzed. CONCLUSION: This study demonstrates an approach that helps to cross the barrier that inhibits data standard adoption in eCRF building and our evaluation reveals the approach has satisfactory performance. Our CDE-based form builder provides an alternative perspective regarding data standard compliant eCRF design.

Root Cause Analysis of Quality Defects Using HPLC-MS Fingerprint Knowledgebase for Batch-to-batch Quality Control of Herbal Drugs.

INTRODUCTION: The batch-to-batch quality consistency of herbal drugs has always been an important issue. OBJECTIVES: To propose a methodology for batch-to-batch quality control based on HPLC-MS fingerprints and process knowledgebase. METHODS: The extraction process of Compound E-jiao Oral Liquid was taken as a case study. After establishing the HPLC-MS fingerprint analysis method, the fingerprints of the extract solutions produced under normal and abnormal operation conditions were obtained. Multivariate statistical models were built for fault detection and a discriminant analysis model was built using the probabilistic discriminant partial-least-squares method for fault diagnosis. RESULTS: Based on multivariate statistical analysis, process knowledge was acquired and the cause-effect relationship between process deviations and quality defects was revealed. The quality defects were detected successfully by multivariate statistical control charts and the type of process deviations were diagnosed correctly by discriminant analysis. CONCLUSION: This work has demonstrated the benefits of combining HPLC-MS fingerprints, process knowledge and multivariate analysis for the quality control of herbal drugs.