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Showing 1 to 6 of 6 for “"disease relationships"”.

  1. Understanding Disease and Disease Relationships Using Transcriptomic Data

    … its potential to deepen our understanding of disease; for example, by revealing gene expression patterns shared between diseases. However, key questions remain around the strength of the transcriptomic signal of disease and the identification of meaningful commonalities between datasets, which …

    cambridge Repository record for Understanding Disease and Disease Relationships Using Transcriptomic Data (opens in a new tab)

  2. Modelling non-linear exposure-disease relationships in a large individual participant meta-analysis allowing for the effects of exposure measurement error

    … meta-analysis of risk factors for coronary heart disease(CHD). Cardiovascular disease is the largest cause of death in almost all countries in the world, therefore it is important to be able to characterise the shape of risk factor–CHD relationships. Many of the risk factors for CHD considered by …

    cambridge Repository record for Modelling non-linear exposure-disease relationships in a large individual participant meta-analysis allowing for the effects of exposure measurement error (opens in a new tab)

  3. Learning diseases from data : a disease space odyssey

    … applied to update the collective knowledge about diseases. Researchers continually study and learn about the complex nature of the human body. They summarize this knowledge with the best possible set of diseases and how those diseases relate to each other. We draw on computational learning theory …

    mit Repository record for Learning diseases from data : a disease space odyssey (opens in a new tab)

  4. Psychosocial and Behavioral Predictors of Energy Intake Plausibility and Weight Loss in Overweight Perimenopausal Women

    … bias from energy misreporting, reduce health or disease risk underestimation and improve understanding of nutrition, health and disease relationships. Further, identification of successful weight loss predictors unique to energy under- and overreporters will enhance weight loss profiling and …

    arizona-thes Repository record for Psychosocial and Behavioral Predictors of Energy Intake Plausibility and Weight Loss in Overweight Perimenopausal Women (opens in a new tab)

  5. Inoculum pattern and relationship between incidence of black root rot of tobacco and inoculum density of Thielaviopsis basicola in field soil

    … of tobacco or chemicals. In order to develop a disease prediction program, disease-inoculum density relationships must be determined. It was the purpose of this study to develop an improved procedure for estimating the populations of T. basicola in naturally infested soil. A second objective was …

    vt Repository record for Inoculum pattern and relationship between incidence of black root rot of tobacco and inoculum density of Thielaviopsis basicola in field soil (opens in a new tab)