Biomedical Data Integration in Computational Drug Design and Bioinformatics

Seoane, José; Aguiar‐Pulido, Vanessa; Munteanu, Cristian R.; Rivero, Daniel; Rabuñal, Juan R.; Dorado, Julián

doi:10.2174/15734099112089990010

Cited by 5 publications

(4 citation statements)

References 101 publications

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“…Once all input data sources are properly encoded to a reduced set of common vocabularies and metadata schemas, and the exploitation rights granted, a knowledge management layer (as depicted in the middle of Figure 3 ) should allow the investigator to first integrate [ 14 ] and then to manage all of the above DHF-research information assets with easy to use and agile knowledge management graphical user web interfaces. The proprietary knowledge management solution assessed during the execution of the Synergy-COPD project [ 15 ] was based on the concept of knowledge as network by abstracting commonly used concepts and knowledge into objects and their relations.…”

Section: Introductionmentioning

confidence: 99%

Biomedical research in a Digital Health Framework

Cano

Lluch-Ariet²,

Gomez-Cabrero

et al. 2014

Journal of Translational Medicine

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This article describes a Digital Health Framework (DHF), benefitting from the lessons learnt during the three-year life span of the FP7 Synergy-COPD project. The DHF aims to embrace the emerging requirements - data and tools - of applying systems medicine into healthcare with a three-tier strategy articulating formal healthcare, informal care and biomedical research. Accordingly, it has been constructed based on three key building blocks, namely, novel integrated care services with the support of information and communication technologies, a personal health folder (PHF) and a biomedical research environment (DHF-research). Details on the functional requirements and necessary components of the DHF-research are extensively presented. Finally, the specifics of the building blocks strategy for deployment of the DHF, as well as the steps toward adoption are analyzed. The proposed architectural solutions and implementation steps constitute a pivotal strategy to foster and enable 4P medicine (Predictive, Preventive, Personalized and Participatory) in practice and should provide a head start to any community and institution currently considering to implement a biomedical research platform.

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Section: Introductionmentioning

confidence: 99%

Biomedical research in a Digital Health Framework

Cano

Lluch-Ariet²,

Gomez-Cabrero

et al. 2014

Journal of Translational Medicine

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“…Some Ontology-based integration tools are available like Ontology Web Language, Extensive Markup Language (XML), RDF Schema or Resource Description Language (RDF), Unified Medical Language System, etc (A Seoane et al. 2013 ). Some weblink based integration tools available like Sequence Retrieval System (Etzold et al.…”

Section: Challengesmentioning

confidence: 99%

“… 2012 , NCBI Entrez, PubChem, Integr8, DisaseCard and EMBL-EBI search and Sequence analysis (A Seoane et al. 2013 ; Madeira et al. 2019 ).…”

Section: Challengesmentioning

confidence: 99%

Machine Learning in Drug Discovery: A Review

Dara¹,

Dhamercherla²,

et al. 2021

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This review provides the feasible literature on drug discovery through ML tools and techniques that are enforced in every phase of drug development to accelerate the research process and deduce the risk and expenditure in clinical trials. Machine learning techniques improve the decision-making in pharmaceutical data across various applications like QSAR analysis, hit discoveries, de novo drug architectures to retrieve accurate outcomes. Target validation, prognostic biomarkers, digital pathology are considered under problem statements in this review. ML challenges must be applicable for the main cause of inadequacy in interpretability outcomes that may restrict the applications in drug discovery. In clinical trials, absolute and methodological data must be generated to tackle many puzzles in validating ML techniques, improving decision-making, promoting awareness in ML approaches, and deducing risk failures in drug discovery.

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“…Bioinformatics and computational biology advances of the last two decades have led to an increase in the number of databases for microbial typing 35 . However, the narrow number of microbial species supported in the database, lack of interoperability, and the proprietary schema of many of these efforts requires new formats that federate this information within a biosurveillance enterprise 41 , 42 . The development of most data management systems focuses on the current state of the technology without considering how their design will affect the legacy of sensors and assays.…”

Section: Biosurveillance Through a Laboratory Networkmentioning

confidence: 99%

Biosurveillance enterprise for operational awareness, a genomic-based approach for tracking pathogen virulence

Valdivia-Granda

2013

Virulence

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To protect our civilians and warfighters against both known and unknown pathogens, biodefense stakeholders must be able to foresee possible technological trends that could affect their threat risk assessment. However, significant flaws in how we prioritize our countermeasure-needs continue to limit their development. As recombinant biotechnology becomes increasingly simplified and inexpensive, small groups, and even individuals, can now achieve the design, synthesis, and production of pathogenic organisms for offensive purposes. Under these daunting circumstances, a reliable biosurveillance approach that supports a diversity of users could better provide early warnings about the emergence of new pathogens (both natural and manmade), reverse engineer pathogens carrying traits to avoid available countermeasures, and suggest the most appropriate detection, prophylactic, and therapeutic solutions. While impressive in data mining capabilities, real-time content analysis of social media data misses much of the complexity in the factual reality. Quality issues within freeform user-provided hashtags and biased referencing can significantly undermine our confidence in the information obtained to make critical decisions about the natural vs. intentional emergence of a pathogen. At the same time, errors in pathogen genomic records, the narrow scope of most databases, and the lack of standards and interoperability across different detection and diagnostic devices, continue to restrict the multidimensional biothreat assessment. The fragmentation of our biosurveillance efforts into different approaches has stultified attempts to implement any new foundational enterprise that is more reliable, more realistic and that avoids the scenario of the warning that comes too late. This discussion focus on the development of genomic-based decentralized medical intelligence and laboratory system to track emerging and novel microbial health threats in both military and civilian settings and the use of virulence factors for risk assessment. Examples of the use of motif fingerprints for pathogen discrimination are provided.

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Biomedical Data Integration in Computational Drug Design and Bioinformatics

Cited by 5 publications

References 101 publications

Biomedical research in a Digital Health Framework

Biomedical research in a Digital Health Framework

Machine Learning in Drug Discovery: A Review

Biosurveillance enterprise for operational awareness, a genomic-based approach for tracking pathogen virulence

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