https://www.nejm.org/doi/full/10.1056/NEJMc1900771?query=featured_secondary

We determined the physiological profile of a 70-year-old male marathoner who ran the event in 2:54:23…

LDL 84mg/dL and HDL 66mg/dL, quite impressive…

https://www.nejm.org/doi/full/10.1056/NEJMc1900771?query=featured_secondary

We determined the physiological profile of a 70-year-old male marathoner who ran the event in 2:54:23…

LDL 84mg/dL and HDL 66mg/dL, quite impressive…

QT:[[”

“The authors describe and validate a citation-based index of ‘disruptiveness’ that has previously been proposed for patents6. The intuition behind the index is straightforward: when the papers that cite a given article also reference a substantial proportion of that article’s references, then the article can be seen as consolidating its scientific domain. When the converse is true — that is, when future citations to the article do not also acknowledge the article’s own intellectual forebears — the article can be seen as disrupting its domain.

The disruptiveness index reflects a characteristic of the article’s underlying content that is clearly distinguishable from impact as conventionally captured by overall citation counts. For instance, the index finds that papers that directly contribute to Nobel prizes tend to exhibit high levels of disruptiveness, whereas, at the other extreme, review articles tend to consolidate their fields.”

“]]

Dynamic Topic Models

https://mimno.infosci.cornell.edu/info6150/readings/dynamic_topic_models.pdf Classic work by @Blei_lab & J Lafferty adapts the #LDA formalism describing documents in terms of latent topics – to allow these to evolve over time

https://my.pgp-hms.org/profile/hu43860C

George Church discloses a lot of his medical records

Timing, rates & spectra of human germline mutation

http://www.nature.com/ng/journal/v48/n2/full/ng.3469.html Metaanalysis of >6500 events gives a de novo mutational signature

Model-Based Approach to Inferring…#Cancer Mutation Signatures http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1005657 Assuming independence betw 3 NTs, 11 v 95 parameters

QT:{{”

The first contribution of this paper is to suggest a more parsimonious approach to modelling mutation signatures, with the benefit of producing both more stable estimates and more easily interpretable signatures. In brief, we substantially reduce the number of parameters per signature by breaking each mutation pattern into “features”, and assuming independence across mutation features. For example, consider the case where a mutation pattern is defined by the substitution and its two flanking bases. We break this into three features

(substitution, 3′ base, 5′ base), and characterize each mutation signature by a probability distribution for each feature (which, by our independence assumption, are multiplied together to define a distribution on mutation patterns). Since the number of possible values for each feature is 6, 4, and 4 respectively this requires 5 + 3 + 3 = 11 parameters instead of 96 − 1 = 95 parameters. Furthermore, extending this model to account for ±n neighboring bases requires only 5 + 6nparameters instead of 6 × 42n − 1. For example, considering ±2 positions requires 17 parameters instead of 1,535. Finally,

incorporating transcription strand as an additional feature adds just one parameter, instead of doubling the number of parameters. “}}

Neutral tumor #evolution across #cancer types

http://www.nature.com/ng/journal/v48/n3/full/ng.3489.html Initial burst of driver events followed by random mutations

KiB v kb, 1024 v 1000. Appears powers of 10 win out over powers of 2 http://www.quora.com/Where-do-we-use-1-kB-1000-bytes-1-MB-1000-kB-1-GB-1000-MB-1-TB-1000-GB-And-where-do-we-use-1-KB-1024-bytes-1-MB-1024-KB-1-GB-1024-MB-1-TB-1024-GB