Re: Journal ref for applied Bayesian Analysis

From: sean kim (sean_incali_at_yahoo.com)
Date: 11/16/04 

Date: 16 Nov 2004 02:53:56 -0800

I'm a bio grad stu.

i don't know much about baeysian networks and its application to mol
bio but i have come across a few.

for instance. from the pubmed database. (journal titles first then
title of the articles and then name of the athors folowed by
abstracts)

Mol Biotechnol. 2004 Nov;28(3):205-26. Links

Joint oligogenic segregation and linkage analysis using bayesian
markov chain monte carlo methods.

Wijsman EM, Yu D.

Division of Medical Genetics, Department of Biostatistics, University
of Washington, Box 357720, Seattle, WA 98195-7720.

One of the most challenging areas in human genetics is the dissection
of quantitative traits. In this context, the efficient use of
available data is important, including, when possible, use of large
pedigrees and many markers for gene mapping. In addition, methods that
jointly perform linkage analysis and estimation of the trait model are
appealing because they combine the advantages of a model-based
analysis with the advantages of methods that do not require
prespecification of model parameters for linkage analysis. Here we
review a Markov chain Monte Carlo approach for such joint linkage and
segregation analysis, which allows analysis of oligogenic traits in
the context of multipoint linkage analysis of large pedigrees. We
provide an outline for practitioners of the salient features of the
method, interpretation of the results, effect of violation of
assumptions, and an example analysis of a two-locus trait to
illustrate the method.

Trends Neurosci. 2004 Dec;27(12):712-9. Links
The Bayesian brain: the role of uncertainty in neural coding and
computation.
Knill DC, Pouget A.

Center for Visual Science and the Department of Brain and Cognitive
Science, University of Rochester, NY 14627, USA.

To use sensory information efficiently to make judgments and guide
action in the world, the brain must represent and use information
about uncertainty in its computations for perception and action.
Bayesian methods have proven successful in building computational
theories for perception and sensorimotor control, and psychophysics is
providing a growing body of evidence that human perceptual
computations are 'Bayes' optimal'. This leads to the 'Bayesian coding
hypothesis': that the brain represents sensory information
probabilistically, in the form of probability distributions. Several
computational schemes have recently been proposed for how this might
be achieved in populations of neurons. Neurophysiological data on the
hypothesis, however, is almost non-existent. A major challenge for
neuroscientists is to test these ideas experimentally, and so
determine whether and how neurons code information about sensory
uncertainty.

Bioinformatics. 2004 Oct 12 [Epub ahead of print] Related Articles,
Links

Bayesian analysis of signaling networks governing embryonic stem cell
fate decisions.

Woolf PJ, Prudhomme W, Daheron L, Daley GQ, Lauffenburger DA.

Biological Engineering Division, Massachusetts Institute of
Technology, Cambridge, MA 02139.

MOTIVATION: Signaling events that direct mouse embryonic stem (ES)
cell self-renewal and differentiation are complex and accordingly
difficult to understand in an integrated manner. We address this
problem by adapting a Bayesian network learning algorithm to model
proteomic signaling data for ES cell fate responses to external cues.
Using this model we are able to characterize signaling pathway
influences as quantitative, logic-circuit type interactions. Our
experimental dataset includes measurements for 28 signaling protein
phosphorylation states across 16 different factorial combinations of
cytokine and matrix stimuli, previously reported by Prudhomme et al.
[Proc. Natl. Acad. Sci. USA (2004)]. RESULTS: The Bayesian network
modeling approach allows us to uncover previously-reported signaling
activities related to mouse ES cell self-renewal, such as the roles of
LIF and STAT3 in maintaining undifferentiated ES cell populations.
Furthermore the network predicts novel influences such as between ERK
phosphorylation and differentiation, or RAF phosphorylation and
differentiated cell proliferation. Visualization of the influences
detected by the Bayesian network provides intuition about the
underlying physiology of the signaling pathways. We demonstrate the
Bayesian networks can capture the linear, nonlinear, and multi-state
logic interactions that connect extracellular cues, intracellular
signals, and consequent cell functional responses. AVAILABILITY:
Datasets and software are available online from
http://www.mit.edu/~pwoolf/mouseES/.

PMID: 15479714 [PubMed - as supplied by publisher]

Bioinformatics. 2004 Sep 7 [Epub ahead of print] Related Articles,
Links
A Bayesian approach to reconstructing genetic regulatory networks with
hidden factors.

Beal MJ, Falciani F, Ghahramani Z, Rangel C, Wild DL.

Department of Computer Science & Engineering, State University of New
York at Buffalo, 201 Bell Hall, Buffalo, NY, 14260-2000, USA.

MOTIVATION: We have used state-space models to reverse engineer
transcriptional networks from highly replicated gene expression
profiling time series data obtained from a well-established model of T
cell activation. State-space models are a class of dynamic Bayesian
networks in which the observed measurements depend on some hidden
state variables that evolve according to Markovian dynamics. These
hidden variables can capture effects which cannot be directly measured
in a gene expression profiling experiment, for example: genes that
have not been included in the microarray, levels of regulatory
proteins, the effects of mRNA and protein degradation etc. RESULTS: We
have approached the problem of inferring the model structure of these
state-space models using both classical and Bayesian methods. In our
previous work, a bootstrap procedure was used to derive classical
confidence intervals for parameters representing 'gene-gene'
interactions over time. In this paper variational approximations are
used to perform the analogous model selection task in the Bayesian
context. Certain interactions are present in both the classical and
the Bayesian analyses of these regulatory networks. The resulting
models place JunB and JunD at the centre of the mechanisms that
control apoptosis and proliferation. These mechanisms are key for
clonal expansion and for controlling the long term behavior (e.g.
programmed cell death) of these cells. AVAILABILITY: Supplementary
data is available at http://public.kgi.edu/~wild/index.htm and Matlab
source code for variational Bayesian learning of state-space models is
available at http://www.cs.toronto.edu/~beal/software.html.

abve are found by typing bayesian networks into pubmed database.

http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed

hope that helps

sean

Bob Ehrlich <bobehrlich@comcast.net> wrote in message news:<Fr6dnXSI6s9UdgXcRVn-gg@comcast.com>...
> Having lurked and learned in this forum for a number of years, I have
> begun to admit that the "old fashioned" frequentest statistical
> procedures that I engage in to this day are flawed in concept. Dr.
> Rubin's and others arguments are slowly sinking in. Now is the time for
> me to read some journal articles written by Baysean practitioners who
> are not professional statisticians. Such papers should not be tutorials
> but should be an attempt to solve a problem in science or engineering.
> Please recommend some gems.

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