• Bioinformatics
• Biomarkers
• Epigenetics
• Neurodegenerative
• Phytobiologicals
• Systems biology

• databases
• research management

• 101016233 EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)
• 101016233 EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)
• 101016233 EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)
• 101016233 EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)
• 832-06g National Council for Eurasian and East European Research (NCEEER)

• antimicrobial peptides
• antimicrobial resistance
• aptamers
• bacteriophages
• companion diagnostics

• asymmetric cross-validation
• data integration
• leave-one-out error

• Humans
• Models, Statistical
• Proportional Hazards Models
• Computer Simulation
• Bias
• Research Design

• T32 CA083654 NCI NIH HHS
• T32 HG000040 NHGRI NIH HHS
• R01 DK129539 NIDDK NIH HHS
• HHSH250-2019-00001C HRSA HHS

• Dimension reduction
• Functionally similarities
• Gene ontology
• Genetic heterogeneity
• Neurodevelopmental disorders
• Semantic similarity
• Unsupervised learning

• Humans
• Gene Ontology
• Genetic Heterogeneity
• Autism Spectrum Disorder/genetics
• Software

• biomedical
• clinical governance
• diagnostic molecular pathology
• diagnostic services
• financial support
• genetic services
• genetic testing
• health care innovation
• health technology
• technology assessment

(1) Background: The data from independent gene expression sources may be integrated for the purpose of molecular diagnostics of cancer. So far, multiple approaches were described. Here, we investigated the impacts of different data fusion strategies on classification accuracy and feature selection stability, which allow the costs of diagnostic tests to be reduced. (2) Methods: We used molecular features (gene expression) combined with a feature extracted from the independent clinical data describing a patient’s sample. We considered the dependencies between selected features in two data fusion strategies (early fusion and late fusion) compared to classification models based on molecular features only. We compared the best accuracy classification models in terms of the number of features, which is connected to the potential cost reduction of the diagnostic classifier. (3) Results: We show that for thyroid cancer, the extracted clinical feature is correlated with (but not redundant to) the molecular data. The usage of data fusion allows a model to be obtained with similar or even higher classification quality (with a statistically significant accuracy improvement, a p-value below 0.05) and with a reduction in molecular dimensionality of the feature space from 15 to 3–8 (depending on the feature selection method). (4) Conclusions: Both strategies give comparable quality results, but the early fusion method provides better feature selection stability.

• Algorithms
• Humans
• Thyroid Neoplasms/diagnosis
• Thyroid Neoplasms/genetics

• artificial intelligence approaches
• big data
• biological analysis
• data mining
• information reuse

• Big Data
• Cloud Computing
• Data Mining/methods
• Machine Learning
• Neural Networks, Computer

• genetic databases
• machine learning
• drug discovery

• Anti-Bacterial Agents/pharmacology
• Drug Resistance, Bacterial/genetics
• Escherichia coli/genetics
• Escherichia coli Infections
• Humans
• Salmonella enterica/genetics

• P42 ES004699 NIEHS NIH HHS
• U.S. Department of Health & Human Services | NIH | National Institute of Environmental Health Sciences (NIEHS)
• United States Department of Agriculture | National Institute of Food and Agriculture (NIFA)

• data integration
• statistical methods

• Exposome
• National genomic projects
• Normal genomic variation
• Pathological genomic variation
• Personalized medicine
• Population genomics
• Precision medicine

• Genetic Diseases, Inborn/diagnosis
• Genetic Diseases, Inborn/epidemiology
• Genetic Diseases, Inborn/genetics
• Genome, Human/genetics
• Genomics
• Humans
• Information Dissemination
• Neoplasms/diagnosis
• Neoplasms/epidemiology
• Neoplasms/genetics
• Precision Medicine

• Javna Agencija za Raziskovalno Dejavnost RS

• IDR
• MongoDB
• NoSQL document store
• NoSQL-based integration framework
• Saudi Arabia
• Saudi genetic research
• clinical data integration
• genetic data
• integrated data repository

Recently high-throughput technologies have been massively used alongside clinical tests to study various types of cancer. Data generated in such large-scale studies are heterogeneous, of different types and formats. With lack of effective integration strategies novel models are necessary for efficient and operative data integration, where both clinical and molecular information can be effectively joined for storage, access and ease of use. Such models, combined with machine learning methods for accurate prediction of survival time in cancer studies, can yield novel insights into disease development and lead to precise personalized therapies.

We developed an approach for intelligent data integration of two cancer datasets (breast cancer and neuroblastoma) − provided in the CAMDA 2018 ‘Cancer Data Integration Challenge’, and compared models for prediction of survival time. We developed a novel semantic network-based data integration framework that utilizes NoSQL databases, where we combined clinical and expression profile data, using both raw data records and external knowledge sources. Utilizing the integrated data we introduced Tumor Integrated Clinical Feature (TICF) − a new feature for accurate prediction of patient survival time. Finally, we applied and validated several machine learning models for survival time prediction.

We developed a framework for semantic integration of clinical and omics data that can borrow information across multiple cancer studies. By linking data with external domain knowledge sources our approach facilitates enrichment of the studied data by discovery of internal relations. The proposed and validated machine learning models for survival time prediction yielded accurate results.

This article was reviewed by Eran Elhaik, Wenzhong Xiao and Carlos Loucera.

• Breast cancer
• Machine learning
• Neuroblastoma
• Semantic data integration
• Survival time prediction

• translational research
• standards
• data integration
• research management

• Humans
• Information Dissemination
• Medical Informatics
• Metadata
• Translational Research, Biomedical
• User-Computer Interface

• BB/I000771/1 Biotechnology and Biological Sciences Research Council
• 115446 & 115308 Innovative Medicines Initiative (IMI)
• BB/E025080/1 Biotechnology and Biological Sciences Research Council
• 115446 Innovative Medicines Initiative (IMI)
• 115308 Innovative Medicines Initiative (IMI)

• TRANSFoRm project
• interoperability
• learning health system
• primary care
• requirements

Finding relevant datasets is important for promoting data reuse in the biomedical domain, but it is challenging given the volume and complexity of biomedical data. Here we describe the development of an open source biomedical data discovery system called DataMed, with the goal of promoting the building of additional data indexes in the biomedical domain.

DataMed, which can efficiently index and search diverse types of biomedical datasets across repositories, is developed through the National Institutes of Health–funded biomedical and healthCAre Data Discovery Index Ecosystem (bioCADDIE) consortium. It consists of 2 main components: (1) a data ingestion pipeline that collects and transforms original metadata information to a unified metadata model, called DatA Tag Suite (DATS), and (2) a search engine that finds relevant datasets based on user-entered queries. In addition to describing its architecture and techniques, we evaluated individual components within DataMed, including the accuracy of the ingestion pipeline, the prevalence of the DATS model across repositories, and the overall performance of the dataset retrieval engine.

Our manual review shows that the ingestion pipeline could achieve an accuracy of 90% and core elements of DATS had varied frequency across repositories. On a manually curated benchmark dataset, the DataMed search engine achieved an inferred average precision of 0.2033 and a precision at 10 (P@10, the number of relevant results in the top 10 search results) of 0.6022, by implementing advanced natural language processing and terminology services. Currently, we have made the DataMed system publically available as an open source package for the biomedical community.

• data discovery index
• dataset
• information dissemination
• information storage and retrieval
• metadata

• Databases, Genetic
• Decision Support Systems, Clinical
• Genomics
• Humans
• Pharmacogenomic Testing
• Precision Medicine

The rise of genomically targeted therapies and immunotherapy has revolutionized the practice of oncology in the last 10–15 years. At the same time, new technologies and the electronic health record (EHR) in particular have permeated the oncology clinic. Initially designed as billing and clinical documentation systems, EHR systems have not anticipated the complexity and variety of genomic information that needs to be reviewed, interpreted, and acted upon on a daily basis. Improved integration of cancer genomic data with EHR systems will help guide clinician decision making, support secondary uses, and ultimately improve patient care within oncology clinics. Some of the key factors relating to the challenge of integrating cancer genomic data into EHRs include: the bioinformatics pipelines that translate raw genomic data into meaningful, actionable results; the role of human curation in the interpretation of variant calls; and the need for consistent standards with regard to genomic and clinical data. Several emerging paradigms for integration are discussed in this review, including: non-standardized efforts between individual institutions and genomic testing laboratories; “middleware” products that portray genomic information, albeit outside of the clinical workflow; and application programming interfaces that have the potential to work within clinical workflow. The critical need for clinical-genomic knowledge bases, which can be independent or integrated into the aforementioned solutions, is also discussed.

• Data integration
• Ordinary least squares
• Structured data
• Sufficient dimension reduction
• Variable selection

• Biomedical laboratory
• Cognition
• Industry–academia collaboration
• Informatics implementation
• Information management
• Virtual research environment

• Anthropology, Cultural/methods
• Biomedical Research/methods
• Biomedical Research/organization & administration
• Cognition
• Community-Institutional Relations
• Cooperative Behavior
• Humans
• Information Management/methods
• Information Systems
• Laboratories
• Reproducibility of Results
• Software
• Technology
• Universities

• R01 EY024249 NEI NIH HHS
• R42 CA105217 NCI NIH HHS
• 1R41CA105217-01A1-STTR NCI NIH HHS

• Animals
• Data Mining/methods
• Genomics/methods
• Humans
• Mice
• Molecular Sequence Annotation/methods
• Proteomics/methods
• Semantics
• Software

• Clinical decision support
• genomic medicine
• personalized health care
• pharmacogenomics
• precision medicine

We live in the genomic era of medicine, where a patient's genomic/molecular data is becoming increasingly important for disease diagnosis, identification of targeted therapy, and risk assessment for adverse reactions. However, decoding the genomic test results and integrating it with clinical data for retrospective studies and cohort identification for prospective clinical trials is still a challenging task. In order to overcome these barriers, we developed an overarching enterprise informatics framework for translational research and personalized medicine called Synergistic Patient and Research Knowledge Systems (SPARKS) and a suite of tools called Oncology Data Retrieval Systems (OncDRS). OncDRS enables seamless data integration, secure and self-navigated query and extraction of clinical and genomic data from heterogeneous sources. Within a year of release, the system has facilitated more than 1500 research queries and has delivered data for more than 50 research studies.

• Clinical & genomic data integration
• Clinical and translational informatics
• Genomic profile
• Next generation sequencing data
• Precision medicine

• Chronic diseases
• Gastrointestinal diseases
• Liver disease
• Nutritional genomics
• Obesity

• Digestive System Diseases/diagnosis
• Digestive System Diseases/genetics
• Digestive System Diseases/therapy
• Gastroenterology/methods
• Gene-Environment Interaction
• Genetic Markers
• Genetic Predisposition to Disease
• Genetic Testing
• Genomics
• Humans
• Phenotype
• Precision Medicine
• Predictive Value of Tests
• Prognosis
• Risk Factors
• Specialization

The ability to query many independent biological databases using a common ontology-based semantic model would facilitate deeper integration and more effective utilization of these diverse and rapidly growing resources. Despite ongoing work moving toward shared data formats and linked identifiers, significant problems persist in semantic data integration in order to establish shared identity and shared meaning across heterogeneous biomedical data sources.

We present five processes for semantic data integration that, when applied collectively, solve seven key problems. These processes include making explicit the differences between biomedical concepts and database records, aggregating sets of identifiers denoting the same biomedical concepts across data sources, and using declaratively represented forward-chaining rules to take information that is variably represented in source databases and integrating it into a consistent biomedical representation. We demonstrate these processes and solutions by presenting KaBOB (the Knowledge Base Of Biomedicine), a knowledge base of semantically integrated data from 18 prominent biomedical databases using common representations grounded in Open Biomedical Ontologies. An instance of KaBOB with data about humans and seven major model organisms can be built using on the order of 500 million RDF triples. All source code for building KaBOB is available under an open-source license.

KaBOB is an integrated knowledge base of biomedical data representationally based in prominent, actively maintained Open Biomedical Ontologies, thus enabling queries of the underlying data in terms of biomedical concepts (e.g., genes and gene products, interactions and processes) rather than features of source-specific data schemas or file formats. KaBOB resolves many of the issues that routinely plague biomedical researchers intending to work with data from multiple data sources and provides a platform for ongoing data integration and development and for formal reasoning over a wealth of integrated biomedical data.

The online version of this article (doi:10.1186/s12859-015-0559-3) contains supplementary material, which is available to authorized users.

This paper presents a system for declaratively transforming medical subjects' data into a common data model representation. Our work is part of the “GAAIN” project on Alzheimer's disease data federation across multiple data providers. We present a general purpose data transformation system that we have developed by leveraging the existing state-of-the-art in data integration and query rewriting. In this work we have further extended the current technology with new formalisms that facilitate expressing a broader range of data transformation tasks, plus new execution methodologies to ensure efficient data transformation for disease datasets.

• Alzheimer's disease datasets
• data integration
• data mapping
• query rewriting

• data integration
• data interoperability
• data sharing
• knowledge discovery
• semantic web
• translational medicine

• Algorithms
• Computational Biology/methods
• Humans
• Information Dissemination/methods
• Internet
• Translational Research, Biomedical

• Bio-repository
• Honest broker
• Integrated data repository
• Meaningful use
• Patient registry
• i2b2

• Algorithms
• Computational Biology/methods
• Genomics
• Mental Disorders/genetics
• Proteomics/methods
• Systems Biology

• Data model
• Database
• Neuroimaging
• Provenance
• Web services
• XCEDE

• Database Management Systems/organization & administration
• Database Management Systems/standards
• Databases, Factual/standards
• Humans
• Informatics/methods
• Informatics/standards
• Informatics/trends
• Information Dissemination/methods
• Internet
• Neuroimaging/methods
• Neuroimaging/standards

• RC4 NS073008-01 NINDS NIH HHS
• 1 U24 U24 RR021992 NCRR NIH HHS
• T15 LM007442 NLM NIH HHS
• RC4 NS073008 NINDS NIH HHS
• U24 RR021992 NCRR NIH HHS
• U24 RR025736 NCRR NIH HHS
• K01 AG035061 NIA NIH HHS
• 1 U24 RR025736-01 NCRR NIH HHS

The field of clinical research informatics includes creation of clinical data repositories (CDRs) used to conduct quality improvement (QI) activities and comparative effectiveness research (CER). Ideally, CDR data are accurately and directly abstracted from disparate electronic health records (EHRs), across diverse health-systems.

Investigators from Washington State’s Surgical Care Outcomes and Assessment Program (SCOAP) Comparative Effectiveness Research Translation Network (CERTAIN) are creating such a CDR. This manuscript describes the automation and validation methods used to create this digital infrastructure.

SCOAP is a QI benchmarking initiative. Data are manually abstracted from EHRs and entered into a data management system. CERTAIN investigators are now deploying Caradigm’s Amalga™ tool to facilitate automated abstraction of data from multiple, disparate EHRs. Concordance is calculated to compare data automatically to manually abstracted. Performance measures are calculated between Amalga and each parent EHR. Validation takes place in repeated loops, with improvements made over time. When automated abstraction reaches the current benchmark for abstraction accuracy - 95% - itwill ‘go-live’ at each site.

A technical analysis was completed at 14 sites. Five sites are contributing; the remaining sites prioritized meeting Meaningful Use criteria. Participating sites are contributing 15–18 unique data feeds, totaling 13 surgical registry use cases. Common feeds are registration, laboratory, transcription/dictation, radiology, and medications. Approximately 50% of 1,320 designated data elements are being automatically abstracted—25% from structured data; 25% from text mining.

In semi-automating data abstraction and conducting a rigorous validation, CERTAIN investigators will semi-automate data collection to conduct QI and CER, while advancing the Learning Healthcare System.

• CERTAIN
• Comparative Effectiveness
• Health Information Technology
• Informatics
• Quality Improvement

The completion of sequencing the human genome in 2003 has spurred the production and collection of genetic data at ever increasing rates. Genetic data obtained for clinical purposes, as is true for all results of clinical tests, are expected to be included in patients’ medical records. With this explosion of information, questions of what, when, where and how to incorporate genetic data into electronic health records (EHRs) have reached a critical point. In order to answer these questions fully, this paper addresses the ethical, logistical and technological issues involved in incorporating these data into EHRs.

This paper reviews journal articles, government documents and websites relevant to the ethics, genetics and informatics domains as they pertain to EHRs.

The authors explore concerns and tasks facing health information technology (HIT) developers at the intersection of ethics, genetics, and technology as applied to EHR development.

By ensuring the efficient and effective incorporation of genetic data into EHRs, HIT developers will play a key role in facilitating the delivery of personalized medicine.

• Electronic Health Records
• Ethics, Medical
• Genomics
• Individualized Medicine
• Medical Informatics

• Brain Neoplasms/genetics
• Brain Neoplasms/metabolism
• Cluster Analysis
• Computational Biology/methods
• Databases, Factual
• Gene Expression Profiling
• Glioma/genetics
• Glioma/metabolism
• Glycolysis
• Humans
• Magnetic Resonance Spectroscopy
• Metabolome
• Support Vector Machine

Drug safety issues pose serious health threats to the population and constitute a major cause of mortality worldwide. Due to the prominent implications to both public health and the pharmaceutical industry, it is of great importance to unravel the molecular mechanisms by which an adverse drug reaction can be potentially elicited. These mechanisms can be investigated by placing the pharmaco-epidemiologically detected adverse drug reaction in an information-rich context and by exploiting all currently available biomedical knowledge to substantiate it. We present a computational framework for the biological annotation of potential adverse drug reactions. First, the proposed framework investigates previous evidences on the drug-event association in the context of biomedical literature (signal filtering). Then, it seeks to provide a biological explanation (signal substantiation) by exploring mechanistic connections that might explain why a drug produces a specific adverse reaction. The mechanistic connections include the activity of the drug, related compounds and drug metabolites on protein targets, the association of protein targets to clinical events, and the annotation of proteins (both protein targets and proteins associated with clinical events) to biological pathways. Hence, the workflows for signal filtering and substantiation integrate modules for literature and database mining, in silico drug-target profiling, and analyses based on gene-disease networks and biological pathways. Application examples of these workflows carried out on selected cases of drug safety signals are discussed. The methodology and workflows presented offer a novel approach to explore the molecular mechanisms underlying adverse drug reactions.

Adverse drug reactions (ADRs) constitute a major cause of morbidity and mortality worldwide. Due to the relevance of ADRs for both public health and pharmaceutical industry, it is important to develop efficient ways to monitor ADRs in the population. In addition, it is also essential to comprehend why a drug produces an adverse effect. To unravel the molecular mechanisms of ADRs, it is necessary to consider the ADR in the context of current biomedical knowledge that might explain it. Nowadays there are plenty of information sources that can be exploited in order to accomplish this goal. Nevertheless, the fragmentation of information and, more importantly, the diverse knowledge domains that need to be traversed, pose challenges to the task of exploring the molecular mechanisms of ADRs. We present a novel computational framework to aid in the collection and exploration of evidences that support the causal inference of ADRs detected by mining clinical records. This framework was implemented as publicly available tools integrating state-of-the-art bioinformatics methods for the analysis of drugs, targets, biological processes and clinical events. The availability of such tools for in silico experiments will facilitate research on the mechanisms that underlie ADR, contributing to the development of safer drugs.

• Computer Simulation
• Database Management Systems
• Databases, Factual
• Documentation/methods
• Drug-Related Side Effects and Adverse Reactions/classification
• Drug-Related Side Effects and Adverse Reactions/epidemiology
• Humans
• Information Storage and Retrieval/methods
• Models, Biological
• Registries

• data bus
• data warehouse
• emr extract

• Biomedical Research/organization & administration
• Database Management Systems
• Databases as Topic/organization & administration
• Electronic Health Records/organization & administration
• Humans
• Information Storage and Retrieval
• Software Design
• Systems Integration

• UL1 RR025780 NCRR NIH HHS
• UL1RR025780 NCRR NIH HHS

• Biological Specimen Banks/standards
• Confidentiality
• Genetic Privacy
• Guidelines as Topic
• Information Dissemination
• Public Opinion
• Risk
• Risk Management
• Weights and Measures

• R01 LM009989 NLM NIH HHS
• U01 HG004603 NHGRI NIH HHS
• 1U011HG004603 NHGRI NIH HHS
• 1R01LM009989 NLM NIH HHS

• translational bioinformatics
• systems medicine
• systems biology
• bioinformatics
• biomedical informatics
• knowledge representation
• information retrieval
• phylogenetics
• modeling physiologic and disease processes
• linking the genotype and phenotype
• identifying genome and protein structure and function
• visualization of data and knowledge
• simulation of complex systems (at all levels: molecules to work groups to organizations)
• knowledge representations
• uncertain reasoning and decision theory
• languages
• computational methods
• statistical analysis of large datasets
• advanced algorithms
• discovery
• text and data mining methods
• natural-language processing
• automated learning
• ontologies

• Computational Biology
• Human Genome Project
• Humans
• Interprofessional Relations
• Medical Informatics
• Translational Research, Biomedical

• T15LM07442 NLM NIH HHS
• U54 HL108460 NHLBI NIH HHS
• T15 LM007442 NLM NIH HHS
• R01HS019913 AHRQ HHS
• UL1 RR024999 NCRR NIH HHS
• R01HG02288 NHGRI NIH HHS
• R01 HS019913 AHRQ HHS
• UL1RR025014 NCRR NIH HHS
• UL1 RR025014 NCRR NIH HHS
• K22 LM008308 NLM NIH HHS
• R01 LM009719 NLM NIH HHS
• P41LM007242 NLM NIH HHS
• U54HL108460 NHLBI NIH HHS
• R01LM009725 NLM NIH HHS
• R01 HG002288 NHGRI NIH HHS
• UL1 RR031980 NCRR NIH HHS
• UL1RR031980 NCRR NIH HHS
• R01 LM009725 NLM NIH HHS
• R01LM009520 NLM NIH HHS
• UL1RR024999 NCRR NIH HHS
• R01LM009719 NLM NIH HHS
• R01 LM009520 NLM NIH HHS