1,156 research outputs found
Reasoning about Independence in Probabilistic Models of Relational Data
We extend the theory of d-separation to cases in which data instances are not
independent and identically distributed. We show that applying the rules of
d-separation directly to the structure of probabilistic models of relational
data inaccurately infers conditional independence. We introduce relational
d-separation, a theory for deriving conditional independence facts from
relational models. We provide a new representation, the abstract ground graph,
that enables a sound, complete, and computationally efficient method for
answering d-separation queries about relational models, and we present
empirical results that demonstrate effectiveness.Comment: 61 pages, substantial revisions to formalisms, theory, and related
wor
Inferring dynamic genetic networks with low order independencies
In this paper, we propose a novel inference method for dynamic genetic
networks which makes it possible to face with a number of time measurements n
much smaller than the number of genes p. The approach is based on the concept
of low order conditional dependence graph that we extend here in the case of
Dynamic Bayesian Networks. Most of our results are based on the theory of
graphical models associated with the Directed Acyclic Graphs (DAGs). In this
way, we define a minimal DAG G which describes exactly the full order
conditional dependencies given the past of the process. Then, to face with the
large p and small n estimation case, we propose to approximate DAG G by
considering low order conditional independencies. We introduce partial qth
order conditional dependence DAGs G(q) and analyze their probabilistic
properties. In general, DAGs G(q) differ from DAG G but still reflect relevant
dependence facts for sparse networks such as genetic networks. By using this
approximation, we set out a non-bayesian inference method and demonstrate the
effectiveness of this approach on both simulated and real data analysis. The
inference procedure is implemented in the R package 'G1DBN' freely available
from the CRAN archive
Protein (Multi-)Location Prediction: Using Location Inter-Dependencies in a Probabilistic Framework
Knowing the location of a protein within the cell is important for
understanding its function, role in biological processes, and potential use as
a drug target. Much progress has been made in developing computational methods
that predict single locations for proteins, assuming that proteins localize to
a single location. However, it has been shown that proteins localize to
multiple locations. While a few recent systems have attempted to predict
multiple locations of proteins, they typically treat locations as independent
or capture inter-dependencies by treating each locations-combination present in
the training set as an individual location-class. We present a new method and a
preliminary system we have developed that directly incorporates
inter-dependencies among locations into the multiple-location-prediction
process, using a collection of Bayesian network classifiers. We evaluate our
system on a dataset of single- and multi-localized proteins. Our results,
obtained by incorporating inter-dependencies are significantly higher than
those obtained by classifiers that do not use inter-dependencies. The
performance of our system on multi-localized proteins is comparable to a top
performing system (YLoc+), without restricting predictions to be based only on
location-combinations present in the training set.Comment: Peer-reviewed and presented as part of the 13th Workshop on
Algorithms in Bioinformatics (WABI2013
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