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Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in different periods. Crossover experiments are generally used in psychology, the pharmaceutical industry, agriculture, and medicine.
Crossover designs are performed even with smaller sample sizes since the samples can act as their controls. These are better than simple randomized trials since patients are exposed to all the treatments.
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Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”
An example of the experimental method is a public...
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A study design is a set of techniques that allow a researcher to collect and analyze data from different variables defined for a specific research problem. Statistics is commonly for effective study design and more robust experiments,
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In cross-sectional research, a researcher compares multiple segments of the population at the same time. If they were interested in people's dietary habits, the researcher might directly compare different groups of people by age. Instead of following a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old...
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Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
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Observational studies are a type of analytical study where researchers observe events without any interventions. In other words, the researcher does not influence the response variable or the experiment's outcome.
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Modelo de datos común estudio-experimento-análisis y bases de datos para la integración y el análisis de datos

Anthony Huffman, Feng-Yu Yeh, Junguk Hur

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    Desarrollamos un modelo de datos común de estudio, experimento y ensayo (SEA) para estandarizar e integrar diversos datos biomédicos. Esto permite nuevos conocimientos sobre las respuestas inmunes, como las diferencias específicas por sexo después de la vacunación contra la gripe.

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    Área de la Ciencia:

    • Informática biomédica
    • Inmunología
    • Ciencia de los datos

    Sus antecedentes:

    • El aumento de los volúmenes de datos experimentales biomédicos heterogéneos plantea desafíos para la estandarización y la integración.
    • Los formatos de datos existentes carecen de interoperabilidad, lo que dificulta el análisis transversal y el descubrimiento de conocimientos.

    Objetivo del estudio:

    • Desarrollar un modelo de datos común (CDM) respaldado por la ontología para estandarizar e integrar diversos datos experimentales biomédicos.
    • Establecer un sistema robusto para la representación, consulta y análisis de datos para descubrir ideas científicas.

    Principales métodos:

    • Desarrolló el modelo de datos común de estudio-experimento (SEA) utilizando principios orientados a objetos y ontologías interoperables.
    • Construyó la base de datos relacional de la Red SEA basada en la ontología (OSEAN) y el gráfico de conocimiento con ETL y herramientas de consulta.
    • Se aplicó el MDC del SEA para representar 1.278 estudios inmunológicos de VIGET, ImmPort y CELLxGENE.

    Principales resultados:

    • Se han representado con éxito más de dos millones de muestras de múltiples recursos de estudios inmunológicos utilizando el MDC de la SEA.
    • Conocimientos científicos identificados sobre las respuestas inmunes específicas del sexo, incluida la desgranulación de neutrófilos y la unión al TNF, después de la vacunación contra la influenza.
    • Demostró la utilidad del MDL para la consulta y el análisis robustos de datos.

    Conclusiones:

    • El CDM SEA respaldado por la ontología proporciona un marco estandarizado para datos biomédicos heterogéneos.
    • El sistema OSEAN facilita la integración de datos y el descubrimiento de conocimientos en los ámbitos biológico y biomédico.
    • Este enfoque sienta las bases para un ecosistema integrador de biodatos.