Already a member? Log in

Sign up with your...

or

Sign Up with your email address

Add Tags

Duplicate Tags

Rename Tags

Share It With Others!

Save Link

Sign in

Sign Up with your email address

Sign up

By clicking the button, you agree to the Terms & Conditions.

Forgot Password?

Please enter your username below and press the send button.
A password reset link will be sent to you.

If you are unable to access the email address originally associated with your Delicious account, we recommend creating a new account.

ADVERTISEMENT
ADVERTISEMENT

Links 1 through 10 of 7798 Pierre Lindenbaum's Bookmarks

I recently gave a hands-on workshop to graduate students in our department about using Twitter in science. As part of that workshop, I provided some bullet points about this social media tool, and I thought it might be useful to share these perspectives more broadly!

Share It With Others!

We used a new tool for exploratory analysis, Mirador, to identify the most informative clinical factors that correlate with EVD outcome. The small sample size and high prevalence of missing records were significant challenges. We applied multiple imputation and bootstrap sampling to address missing data and quantify overfitting. We trained several predictors over all combinations of covariates, which resulted in an ensemble of predictors, with and without viral load information, with an area under the receiver operator characteristic curve of 0.8 or more, after correcting for optimistic bias. We ranked the predictors by their F1-score, and those above a set threshold were compiled into a mobile app, Ebola CARE (Computational Assignment of Risk Estimates).

Share It With Others!

The human reference assembly remains incomplete due to the underrepresentation of repeat-rich sequences that are found within centromeric regions and acrocentric short arms. Although these sequences are marginally represented in the assembly, they are often fully represented in whole-genome short-read datasets and contribute to inappropriate alignments and high read-depth signals that localize to a small number of assembled homologous regions. As a consequence, these regions often provide artifactual peak calls that confound hypothesis testing and large-scale genomic studies. To address this problem, we have constructed mapping targets that represent roughly 8% of the human genome generally omitted from the human reference assembly. By integrating these data into standard mapping and peak-calling pipelines we demonstrate a 10-fold reduction in signals in regions common to the blacklisted region and identify a comprehensive set of regions that exhibit mapping sensitivity with the presence of the repeat-rich targets.

Share It With Others!

We recently released an open-source framework called A-Frame for easily creating 3D and VR experiences on the Web. A-Frame puts VR content creation into our hands by allowing us to create scenes with declarative HTML that just work across desktop, Oculus Rift, and smartphones. We can manipulate scenes with vanilla JavaScript just as we would with ordinary HTML elements, and we can continue using our favorite JavaScript libraries and frameworks (e.g. d3, React). A basic scene in A-Frame looks something like:

Share It With Others!

via @rguha Dr Goldacre appears to believe that sharing the 'analytic code' would in some way improve science. Although clearly not an intended consequence, this suggestion may unfortunately be an example of bad science for the following reasons: First, replication is key to the scientific activity; when analysing our randomised trials for example, our Standard Operating Procedures (SOPs) direct us to replicate the findings completely. This specifically includes the programming steps, to provide independent verification of our findings. We asked a senior colleague to look over our two published papers[1,2] (our paper in the BMJ simply being an encore of the one in JRSM) of this observational study. His report stated that our analyses were described in sufficient detail in the published literature for others to replicate them. Indeed we are aware from our peer review activities that others have successfully replicated our methods in whole or in part for weekend analyses

Share It With Others!

Share It With Others!

Share It With Others!

display a regular expression as a graph

Share It With Others!

Share It With Others!

The Ebola virus disease epidemic in West Africa is the largest on record, responsible for over 28,599 cases and more than 11,299 deaths1. Genome sequencing in viral outbreaks is desirable to characterize the infectious agent and determine its evolutionary rate. Genome sequencing also allows the identification of signatures of host adaptation, identification and monitoring of diagnostic targets, and characterization of responses to vaccines and treatments. The Ebola virus (EBOV) genome substitution rate in the Makona strain has been estimated at between 0.87 × 10−3 and 1.42 × 10−3 mutations per site per year. This is equivalent to 16–27 mutations in each genome, meaning that sequences diverge rapidly enough to identify distinct sub-lineages during a prolonged epidemic2, 3, 4, 5, 6, 7. Genome sequencing provides a high-resolution view of pathogen evolution and is increasingly sought after for outbreak surveillance.

Share It With Others!

ADVERTISEMENT
ADVERTISEMENT
ADVERTISEMENT