Subject: Re: REminder: PHD Project Team Meeting 9/23 1130AM EDT
From: "Lawrence Carin" <[email-masked]>
To: "Tim Veldman" <[email-masked]>, "Aimee Zaas" <[email-masked]>, "Alfred O. Hero" <[email-masked]>, "Brett Caram" <[email-masked]>, "Carolyn Talcott" <[email-masked]>, "Christine Oien" <[email-masked]>, "Christopher W Woods" <[email-masked]>, "Geoff Ginsburg" <[email-masked]>, "Jay B Varkey" <[email-masked]>, "Patrick Lincoln" <[email-masked]>, "Rita L Chambers" <[email-masked]>, "Sara Hoffman" <[email-masked]>, "Stephen Kingsmore" <[email-masked]>
Cc: "'Eley, Greg [USA]'" <[email-masked]>, <[email-masked]>
Date: Tue, 23 Sep 2008 07:51:17 -0400

Attached are Duke analysis results. We have done joint factor analysis =
on the RSV and Rhino data combined (results are generated via two =
distinct inference engines, MCMC and variational Bayes, and we get =
similar results between the two). We also show ROC curves for the RSV =
data based on the sparse probit-regression model; we meet the milestones =
with these ROCs.

I think a nice "story" is coming together.

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From: "Lawrence Carin" <[email-masked]>
To: "Stephen Kingsmore" <[email-masked]>, "Tim Veldman" <[email-masked]>,
        "Aimee Zaas" <[email-masked]>,
        "Alfred O. Hero" <[email-masked]>,
        "Brett Caram" <[email-masked]>,
        "Carolyn Talcott" <[email-masked]>,
        "Christine Oien" <[email-masked]>,
        "Christopher W Woods" <[email-masked]>,
        "Geoff Ginsburg" <[email-masked]>,
        "Jay B Varkey" <[email-masked]>,
        "Patrick Lincoln" <[email-masked]>,
        "Rita L Chambers" <[email-masked]>,
        "Sara Hoffman" <[email-masked]>
Cc: "'Eley, Greg [USA]'" <[email-masked]>, <[email-masked]>
Subject: Re: REminder:  PHD Project Team Meeting 9/23 1130AM EDT
Date: Tue, 23 Sep 2008 16:12:01 -0400

This is a Bayesian analysis, so we infer a full posterior density 
function on the model parameters (the posterior density function 
accounts for our uncertainty, as a result of the limited data). Bayesian 
approaches are particularly useful when the data size is small. Further, 
we used two very different inference engines (MCMC and VB), and they 
yielded similar results. I think the results can be trusted.

That said, our probit-regression model only finds two important genes, 
and it is that model which we use to meet the Phase 1 goals.

  ----- Original Message -----
  From: Stephen Kingsmore
  To: Lawrence Carin ; Tim Veldman ; Aimee Zaas ; Alfred O. Hero ; Brett 
Caram ; Carolyn Talcott ; Christine Oien ; Christopher W Woods ; Geoff 
Ginsburg ; Jay B Varkey ; Patrick Lincoln ; Rita L Chambers ; Sara 
Hoffman
  Cc: 'Eley, Greg [USA]' ; [email-masked]
  Sent: Tuesday, September 23, 2008 3:55 PM
  Subject: Re: REminder: PHD Project Team Meeting 9/23 1130AM EDT

  Hi Guys

  Is it correct that the "factor 26" MCMC solution has 35 genes?  Given 
the small cohort size is there a concern re. overfitting of the data?  
Is much discriminatory power lost if the top 5 or 10 of the "factor 26" 
set are used?

  Thanks

  Stephen

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