Journal club (deferred)

Somatic mutational profiles and germline polygenic risk scores in human cancer

Yuxi Liu, Alexander Gusev, Yujing J. Heng, Ludmil B. Alexandrov & Peter Kraft 

Genome Medicine volume 14, Article number: 14 (2022) 

Following Montse's foundational paper for the PRS calculator https://pubmed.ncbi.nlm.nih.gov/27140283/ 

Hackathon - GPT show and share

Show and Tell

Take me to your leader

Jonas

https://episphere.github.io/gpt/jonas

gpt = await import('https://episphere.github.io/gpt/jonas/export.js')

Jeya

...

APIs

https://platform.openai.com/docs/api-reference 

SDKs and UIs for GPT operation

... this will have to be participated

...

Plugins

alpha users application: https://openai.com/blog/chatgpt-plugins 

Discussion - scalability, operational model, etc

Dialog model - has anyone studied it?

Eric Topol at Colbert report: https://www.cc.com/video/udr4lu/the-colbert-report-eric-topol 

Foundation models: https://ai.googleblog.com/2022/04/pathways-language-model-palm-scaling-to.html 

Phill - joint point analysis

Joint point analysis is a statistical technique used to analyze changes in trends over time by identifying the point at which a significant change occurs. This technique is commonly used in epidemiology to analyze changes in the incidence or prevalence of diseases or health outcomes over time.

In joint point analysis, a linear regression model is used to estimate the annual percentage change in the trend of the outcome variable over a specified time period. The model is then fitted with different numbers of "joint points," or points at which the trend changes significantly. The optimal number of joint points is selected based on statistical criteria, such as the Bayesian Information Criterion (BIC) or the Akaike Information Criterion (AIC).

Joint point analysis provides several measures of trend change, including the annual percentage change (APC) and the estimated time of the joint point. These measures can be used to identify periods of significant change in the trend of the outcome variable, which can inform public health interventions and policy decisions.

Overall, joint point analysis is a useful statistical tool for analyzing changes in trends over time and can provide valuable insights into the epidemiology of diseases and other health outcomes.