Showing papers on "Translational research informatics published in 2020"

Data-driven translational prostate cancer research: from biomarker discovery to clinical decision

Abstract: Prostate cancer (PCa) is a common malignant tumor with increasing incidence and high heterogeneity among males worldwide. In the era of big data and artificial intelligence, the paradigm of biomarker discovery is shifting from traditional experimental and small data-based identification toward big data-driven and systems-level screening. Complex interactions between genetic factors and environmental effects provide opportunities for systems modeling of PCa genesis and evolution. We hereby review the current research frontiers in informatics for PCa clinical translation. First, the heterogeneity and complexity in PCa development and clinical theranostics are introduced to raise the concern for PCa systems biology studies. Then biomarkers and risk factors ranging from molecular alternations to clinical phenotype and lifestyle changes are explicated for PCa personalized management. Methodologies and applications for multi-dimensional data integration and computational modeling are discussed. The future perspectives and challenges for PCa systems medicine and holistic healthcare are finally provided.

Translating Social Determinants of Health into Standardized Clinical Entities

Abstract: Social determinants of health (SDH) are a valuable source of health information which still are not fully utilized in the clinical space. Knowing that a certain patient has trouble finding transportation, has a potentially hazardous relationship with a family member or close relative, is currently unemployed, or various other social factors would allow providers to tailor treatment plans in a way to best help that patient. However, these SDH must be gathered, represented, and stored in a standardized way before they can be leveraged by informatics tools designed for health providers. This process of translating SDH to standardized clinical entities includes two main steps. The first is a collaborative effort to establish an ontology of medical terminology codes (i.e., ICD, SNOMED, LOINC, etc.) which can be used to uniformly represent SDH as coded concepts. The second is a collaborative effort to use the FHIR standard to create profiles and extensions which will allow FHIR resources to be used to store the coded SDH as clinical entities. Each of these steps has their own complexities that must be considered and accounted for in future efforts to create interoperable clinical informatics solutions which utilize SDH.

Contributions from the 2019 Literature on Bioinformatics and Translational Informatics.

Abstract: Objectives: Summarize recent research and select the best papers published in 2019 in the field of Bioinformatics and Translational Informatics (BTI) for the corresponding section of the International Medical Informatics Association Yearbook. Methods: A literature review was performed for retrieving from PubMed papers indexed with keywords and free terms related to BTI. Independent review allowed the section editors to select a list of 15 candidate best papers which were subsequently peer-reviewed. A final consensus meeting gathering the whole Yearbook editorial committee was organized to finally decide on the selection of the best papers. Results: Among the 931 retrieved papers covering the various subareas of BTI, the review process selected four best papers. The first paper presents a logical modeling of cancer pathways. Using their tools, the authors are able to identify two known behaviours of tumors. The second paper describes a deep-learning approach to predicting resistance to antibiotics in Mycobacterium tubercu-losis. The authors of the third paper introduce a Genomic Global Positioning System (GPS) enabling comparison of genomic data with other individuals or genomics databases while preserving privacy. The fourth paper presents a multi-omics and temporal sequence-based approach to provide a better understanding of the sequence of events leading to Alzheimer’s Disease. Conclusions: Thanks to the normalization of open data and open science practices, research in BTI continues to develop and mature. Noteworthy achievements are sophisticated applications of leading edge machine-learning methods dedicated to person-alized medicine.

Translational Informatics Connects Real‐World Information to Knowledge in an Increasingly Data‐Driven World

Abstract: The clinical pharmacology community stands on the cusp of a dataempowered revolution, where health data artifacts ascertained from real-world data sources, including electronic health records (EHRs), patient wearables, genomics, and other ‘omics, hold momentous opportunity to inform our understanding of patient outcomes. The opportunity to advance our molecular understanding of why select patients do not respond to certain therapies in larger, more representative populations will empower future drug discovery and facilitate research.

Adding Value to Translational Informatics through the Semantic Management of Drug to Drug Interaction.

Abstract: Translational informatics, aimed at bridging the gap between biomedical scientific knowledge and clinical practice has changed the way we use rapidly growing information from biomedical research and bring it closer to clinical practice. Software technologies play an important role in this process, particularly if they help in understanding and manipulating the meaning of data and information generated in biomedical research and translate it into semantic suitable for clinical practice. In this paper, we propose software architectural and conceptual computational models, which use semantic technologies in order to explore the meaning of the relationships between drugs when they interact in clinical practice. The data about drug to drug interactions, available from biomedical research, is reusable in instances where they are decisive factors in drug administration in clinical practice. We explore the power of semantic web technologies and SWRL enabled OWL ontologies to demonstrate the applicability and feasibility of our proposal in translational informatics.

Fundamentals of Drug Metabolism and Pharmacogenomics Within a Learning Healthcare System Workflow Perspective

Abstract: This chapter introduces fundamental concepts of pharmacogenomics, including drug metabolism, and provides an informatics workflow-based perspective inspired by a learning healthcare system framework. Our intent is that the reader sees pharmacogenomics as a foundation of precision medicine, which is reliant upon informatics to deliver actionable patient-tailored knowledge at the point of care. Further, pharmacogenomics knowledge is poised to be further developed so as to be amenable to multi-drug comorbid disease treatment necessitated by precision medicine practice. Upon reviewing this chapter, we hope the reader understands how informatics is uniquely suited to i) enhance clinic-based precision medicine practice through appropriate dissemination of patient-tailored actionable pharmacogenomics knowledge, and ii) to advance the knowledge base underpinning pharmacogenomics by gleaning insights from real world outcomes of these same clinic-based populations. The methodologic considerations highlighted within this workflow-base perspective encompass end-to-end forward and reverse translational informatics activities, designed to both appropriately deploy existing pharmacogenomics knowledge, as well as contribute to its advancement by harnessing insights from real-world data.