28 Matching Annotations
  1. Mar 2021
    1. 14 of which were sampled at multiple timepoints
    2. RNA sequencing on samples from 46 individuals with PCR-positive, symptomatic SARS-CoV-2 infection
    3. 77 peripheral blood samples across 46 subjects with COVID-19 and compared them to subjects with seasonal coronavirus, influenza, bacterial pneumonia, and healthy controls.
    4. seasonal coronavirus (n=59)
    5. divided based on disease severity and time from symptom onset
    6. elucidate novel aspects of the host response to SARS-CoV-2
    7. influenza (n=17)
    8. bacterial pneumonia (n=20)
    9. healthy controls (n=19)
    1. elucidate key pathways in the host transcriptome of patients infected with SARS-CoV-2, we used RNA sequencing (RNA Seq) to analyze nasopharyngeal (NP) swab and whole blood (WB) samples from 333 COVID-19 patients and controls, including patients with other viral and bacterial infections.
    2. host response biosignature for COVID-19 from RNA profiling of nasal swabs and blood
  2. Jul 2015
    1. educational uses of annotation technology

      The most important thing in educational use is categorizing your annotation and notes. I left Diigo due to removal of "lists" from their platform and I'm following your project for the past 7 months and yet this platform lack that fundamental feature as well.

  3. Feb 2015
    1. the k-Nearest Neighbors algorithm (or k-NN for short) is a non-parametric method used for classification and regression
    1. Note that the non-parametric model is not none-parametric.
    2. Unlike parametric statistics, nonparametric statistics make no assumptions about the probability distributions of the variables being assessed.

      Why called non-parametrci

    1. Note: creating multiple accounts or teams solely to circumvent limits on the "purchased" in silico data is grounds for disqualification.

      Multi account rules

    2. The goal of the DREAM Olfaction Prediction Challenge is to find models that can predict how a molecule smells from its physical and chemical features.
    1. The use of the term n − 1 is called Bessel's correction, and it is also used in sample covariance and the sample standard deviation (the square root of variance)

      Why in \(\sigma^2\) is not equal to \(s^2\)

    2. Sample variance can also be applied to the estimation of the variance of a continuous distribution from a sample of that distribution.
    1. Suppose the value of for wages is 10% and the values of for kilograms of meat is 25%. This means that the wages of workers are consistent. Their wages are close to the overall average of their wages. But the families consume meat in quite different quantities. Some families use very small quantities of meat and some others use large quantities of meat. We say that there is greater variation in their consumption of meat. The observations about the quantity of meat are more dispersed or more variant.

      Interpretation of Relative Deviation Coefficient

    1. The FDA has also established an acceptable daily intake (ADI) for each artificial sweetener. This is the maximum amount considered safe to consume each day over the course of your lifetime. ADIs are intended to be about 100 times less than the smallest amount that might cause health concerns.

      How is Acceptable Daily Intake calculated by FDA

    2. Artificial sweeteners are regulated by the Food and Drug Administration (FDA) as food additives.
    1. its victims are extremely vulnerable to infectious diseases and some of them, such as David Vetter, have become famous for living in a sterile environment.

      Basic info

    2. SCID is the most severe form of primary immunodeficiencies,[4] and there are now at least nine different known genes in which mutations lead to a form of SCID.[5]
  4. Jan 2015
    1. Probit analysis will produce results similar logistic regression.

      Probit regression vs. Logistic regression

    2. R requires forward slashes (/) not back slashes (\) when specifying a file location even if the file is on your hard drive.
    3. Logistic regression, also called a logit model, is used to model dichotomous outcome variables. In the logit model the log odds of the outcome is modeled as a linear combination of the predictor variables.