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@article{Alexandersson2003SLAM,
author = {Alexandersson, M. and Cawley, S. and Pachter, L.},
title = {S{LAM}: cross-species gene finding and alignment with a generalized
pair hidden {M}arkov model.},
journal = {Genome {R}es.},
year = {2003},
volume = {13},
pages = {496--502},
number = {3},
month = {Mar},
abstract = {Comparative-based gene recognition is driven by the principle that
conserved regions between related organisms are more likely than
divergent regions to be coding. {W}e describe a probabilistic framework
for gene structure and alignment that can be used to simultaneously
find both the gene structure and alignment of two syntenic genomic
regions. {A} key feature of the method is the ability to enhance
gene predictions by finding the best alignment between two syntenic
sequences, while at the same time finding biologically meaningful
alignments that preserve the correspondence between coding exons.
{O}ur probabilistic framework is the generalized pair hidden {M}arkov
model, a hybrid of (1). generalized hidden {M}arkov models, which
have been used previously for gene finding, and (2). pair hidden
{M}arkov models, which have applications to sequence alignment. {W}e
have built a gene finding and alignment program called {SLAM}, which
aligns and identifies complete exon/intron structures of genes in
two related but unannotated sequences of {DNA}. {SLAM} is able to
reliably predict gene structures for any suitably related pair of
organisms, most notably with fewer false-positive predictions compared
to previous methods (examples are provided for {H}omo sapiens/{M}us
musculus and {P}lasmodium falciparum/{P}lasmodium vivax comparisons).
{A}ccuracy is obtained by distinguishing conserved noncoding sequence
({CNS}) from conserved coding sequence. {CNS} annotation is a novel
feature of {SLAM} and may be useful for the annotation of {UTR}s,
regulatory elements, and other noncoding features.},
doi = {10.1101/gr.424203},
pdf = {../local/Alexandersson2003SLAM.pdf},
file = {Alexandersson2003SLAM.pdf:local/Alexandersson2003SLAM.pdf:PDF},
keywords = {biogm},
owner = {vert},
pmid = {12618381},
timestamp = {2006.01.18},
url = {http://dx.doi.org/10.1101/gr.424203}
}
@article{Beal2005Bayesian,
author = {Beal, M. J. and Falciani, F. and Ghahramani, Z. and Rangel, C. and
Wild, D. L.},
title = {A {B}ayesian approach to reconstructing genetic regulatory networks
with hidden factors.},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {349--356},
number = {3},
month = {Feb},
abstract = {M{OTIVATION}: {W}e have used state-space models ({SSM}s) to reverse
engineer transcriptional networks from highly replicated gene expression
profiling time series data obtained from a well-established model
of {T} cell activation. {SSM}s are a class of dynamic {B}ayesian
networks in which the observed measurements depend on some hidden
state variables that evolve according to {M}arkovian dynamics. {T}hese
hidden variables can capture effects that 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 m{RNA} and protein degradation, etc. {RESULTS}: {W}e
have approached the problem of inferring the model structure of these
state-space models using both classical and {B}ayesian methods. {I}n
our previous work, a bootstrap procedure was used to derive classical
confidence intervals for parameters representing 'gene-gene' interactions
over time. {I}n this article, variational approximations are used
to perform the analogous model selection task in the {B}ayesian context.
{C}ertain interactions are present in both the classical and the
{B}ayesian analyses of these regulatory networks. {T}he resulting
models place {J}un{B} and {J}un{D} at the centre of the mechanisms
that control apoptosis and proliferation. {T}hese mechanisms are
key for clonal expansion and for controlling the long term behavior
(e.g. programmed cell death) of these cells. {AVAILABILITY}: {S}upplementary
data is available at http://public.kgi.edu/wild/index.htm and {M}atlab
source code for variational {B}ayesian learning of {SSM}s is available
at http://www.cse.ebuffalo.edu/faculty/mbeal/software.html.},
doi = {10.1093/bioinformatics/bti014},
pdf = {../local/Beal2005Bayesian.pdf},
file = {Beal2005Bayesian.pdf:local/Beal2005Bayesian.pdf:PDF},
keywords = {biogm},
owner = {vert},
pii = {bti014},
pmid = {15353451},
timestamp = {2006.01.18},
url = {http://dx.doi.org/10.1093/bioinformatics/bti014}
}
@article{Engelhardt2005Protein,
author = {Engelhardt, B. E. and Jordan, M. I. and Muratore, K. E. and Brenner,
S. E.},
title = {Protein {M}olecular {F}unction {P}rediction by {B}ayesian {P}hylogenomics.},
journal = {P{L}o{S} {C}omput. {B}iol.},
year = {2005},
volume = {1},
pages = {e45},
number = {5},
month = {Oct},
abstract = {We present a statistical graphical model to infer specific molecular
function for unannotated protein sequences using homology. {B}ased
on phylogenomic principles, {SIFTER} ({S}tatistical {I}nference of
{F}unction {T}hrough {E}volutionary {R}elationships) accurately predicts
molecular function for members of a protein family given a reconciled
phylogeny and available function annotations, even when the data
are sparse or noisy. {O}ur method produced specific and consistent
molecular function predictions across 100 {P}fam families in comparison
to the {G}ene {O}ntology annotation database, {BLAST}, {GO}tcha,
and {O}rthostrapper. {W}e performed a more detailed exploration of
functional predictions on the adenosine-5'-monophosphate/adenosine
deaminase family and the lactate/malate dehydrogenase family, in
the former case comparing the predictions against a gold standard
set of published functional characterizations. {G}iven function annotations
for 3\% of the proteins in the deaminase family, {SIFTER} achieves
96\% accuracy in predicting molecular function for experimentally
characterized proteins as reported in the literature. {T}he accuracy
of {SIFTER} on this dataset is a significant improvement over other
currently available methods such as {BLAST} (75\%), {G}ene{Q}uiz
(64\%), {GO}tcha (89\%), and {O}rthostrapper (11\%). {W}e also experimentally
characterized the adenosine deaminase from {P}lasmodium falciparum,
confirming {SIFTER}'s prediction. {T}he results illustrate the predictive
power of exploiting a statistical model of function evolution in
phylogenomic problems. {A} software implementation of {SIFTER} is
available from the authors.},
doi = {10.1371/journal.pcbi.0010045},
pdf = {../local/Engelhardt2005Protein.pdf},
file = {Engelhardt2005Protein.pdf:local/Engelhardt2005Protein.pdf:PDF},
keywords = {biogm},
owner = {vert},
pmid = {16217548},
timestamp = {2006.01.18},
url = {http://dx.doi.org/10.1371/journal.pcbi.0010045}
}
@article{Friedman2000Using,
author = {Friedman, N. and Linial, M. and Nachman, I. and Pe'er, D.},
title = {Using {B}ayesian Networks to Analyze Expression Data},
journal = {J. Comput. Biol.},
year = {2000},
volume = {7},
pages = {601--620},
number = {3-4},
abstract = {D{NA} hybridization arrays simultaneously measure the expression level
for thousands of genes. {T}hese measurements provide a "snapshot"
of transcription levels within the cell. {A} major challenge in computational
biology is to uncover, from such measurements, gene/protein interactions
and key biological features of cellular systems. {I}n this paper,
we propose a new framework for discovering interactions between genes
based on multiple expression measurements. {T}his framework builds
on the use of {B}ayesian networks for representing statistical dependencies.
{A} {B}ayesian network is a graph-based model of joint multivariate
probability distributions that captures properties of conditional
independence between variables. {S}uch models are attractive for
their ability to describe complex stochastic processes and because
they provide a clear methodology for learning from (noisy) observations.
{W}e start by showing how {B}ayesian networks can describe interactions
between genes. {W}e then describe a method for recovering gene interactions
from microarray data using tools for learning {B}ayesian networks.
{F}inally, we demonstrate this method on the {S}. cerevisiae cell-cycle
measurements of {S}pellman et al. (1998).},
doi = {10.1089/106652700750050961},
pdf = {../local/Friedman2000Using.pdf},
file = {Friedman2000Using.pdf:local/Friedman2000Using.pdf:PDF},
keywords = {biogm},
subject = {microarray},
url = {http://dx.doi.org/10.1089/106652700750050961}
}
@incollection{Heckerman1999tutorial,
author = {Heckerman, D.},
title = {A tutorial on learning with {B}ayesian networks},
booktitle = {Learning in graphical models},
publisher = {MIT Press},
year = {1999},
editor = {Jordan, M.},
pages = {301--354},
address = {Cambridge, MA, USA},
pdf = {../local/Heckerman1999tutorial.pdf},
file = {Heckerman1999tutorial.pdf:local/Heckerman1999tutorial.pdf:PDF},
keywords = {biogm},
owner = {vert},
timestamp = {2006.01.18}
}
@article{Imoto2002Estimation,
author = {Imoto, S. and Goto, T. and Miyano, S.},
title = {Estimation of genetic networks and functional structures between
genes by using {B}ayesian networks and nonparametric regression.},
journal = {Pac. {S}ymp. {B}iocomput.},
year = {2002},
pages = {175--186},
abstract = {We propose a new method for constructing genetic network from gene
expression data by using {B}ayesian networks. {W}e use nonparametric
regression for capturing nonlinear relationships between genes and
derive a new criterion for choosing the network in general situations.
{I}n a theoretical sense, our proposed theory and methodology include
previous methods based on {B}ayes approach. {W}e applied the proposed
method to the {S}. cerevisiae cell cycle data and showed the effectiveness
of our method by comparing with previous methods.},
pdf = {../local/Imoto2002Estimation.pdf},
file = {Imoto2002Estimation.pdf:local/Imoto2002Estimation.pdf:PDF},
keywords = {biogm},
owner = {vert},
pmid = {11928473},
timestamp = {2006.02.16},
url = {http://helix-web.stanford.edu/psb02/imoto.pdf}
}
@article{Imoto2003Bayesian,
author = {Imoto, S. and Kim, S. and Goto, T. and Miyano, S. and Aburatani,
S. and Tashiro, K. and Kuhara, S.},
title = {Bayesian network and nonparametric heteroscedastic regression for
nonlinear modeling of genetic network.},
journal = {J. {B}ioinform. {C}omput. {B}iol.},
year = {2003},
volume = {1},
pages = {231--252},
number = {2},
month = {Jul},
abstract = {We propose a new statistical method for constructing a genetic network
from microarray gene expression data by using a {B}ayesian network.
{A}n essential point of {B}ayesian network construction is the estimation
of the conditional distribution of each random variable. {W}e consider
fitting nonparametric regression models with heterogeneous error
variances to the microarray gene expression data to capture the nonlinear
structures between genes. {S}electing the optimal graph, which gives
the best representation of the system among genes, is still a problem
to be solved. {W}e theoretically derive a new graph selection criterion
from {B}ayes approach in general situations. {T}he proposed method
includes previous methods based on {B}ayesian networks. {W}e demonstrate
the effectiveness of the proposed method through the analysis of
{S}accharomyces cerevisiae gene expression data newly obtained by
disrupting 100 genes.},
doi = {10.1142/S0219720003000071},
pdf = {../local/Imoto2003Bayesian.pdf},
file = {Imoto2003Bayesian.pdf:local/Imoto2003Bayesian.pdf:PDF},
keywords = {biogm},
owner = {vert},
pii = {S0219720003000071},
pmid = {15290771},
timestamp = {2006.02.16},
url = {http://dx.doi.org/10.1142/S0219720003000071}
}
@article{Imoto2002Bayesian,
author = {Imoto, S. and Sunyong, K. and Goto, T. and Aburatani, S. and Tashiro,
K. and Kuhara, S. and Miyano, S.},
title = {Bayesian network and nonparametric heteroscedastic regression for
nonlinear modeling of genetic network.},
journal = {Proc. {IEEE} {C}omput. {S}oc. {B}ioinform. {C}onf.},
year = {2002},
volume = {1},
pages = {219--227},
abstract = {We propose a new statistical method for constructing genetic network
from microarray gene expression data by using a {B}ayesian network.
{A}n essential point of {B}ayesian network construction is in the
estimation of the conditional distribution of each random variable.
{W}e consider fitting nonparametric regression models with heterogeneous
error variances to the microarray gene expression data to capture
the nonlinear structures between genes. {A} problem still remains
to be solved in selecting an optimal graph, which gives the best
representation of the system among genes. {W}e theoretically derive
a new graph selection criterion from {B}ayes approach in general
situations. {T}he proposed method includes previous methods based
on {B}ayesian networks. {W}e demonstrate the effectiveness of the
proposed method through the analysis of {S}accharomyces cerevisiae
gene expression data newly obtained by disrupting 100 genes.},
doi = {10.1109/CSB.2002.1039344},
pdf = {../local/Imoto2002Bayesian.pdf},
file = {Imoto2002Bayesian.pdf:local/Imoto2002Bayesian.pdf:PDF},
keywords = {biogm},
owner = {vert},
pmid = {15838138},
timestamp = {2006.02.16},
url = {http://dx.doi.org/10.1109/CSB.2002.1039344}
}
@article{Jansen2003Bayesian,
author = {Jansen, R. and Yu, H. and Greenbaum, D. and Kluger, Y. and Krogan,
N.J. and Chung, S. and Emili, A. and Snyder, M. and Greenblatt, J.F.
and Gerstein, M.},
title = {A {B}ayesian networks approach for predicting protein-protein interactions
from genomic data},
journal = {Science},
year = {2003},
volume = {302},
pages = {449-453},
number = {5644},
abstract = {We have developed an approach using {B}ayesian networks to predict
protein-protein interactions genome-wide in yeast. {O}ur method naturally
weights and combines into reliable predictions genomic features only
weakly associated with interaction (e.g., m{RNA} coexpression, coessentiality,
and colocalization). {I}n addition to de novo predictions, it can
integrate often noisy, experimental interaction data sets. {W}e observe
that at given levels of sensitivity, our predictions are more accurate
than the existing high-throughput experimental data sets. {W}e validate
our predictions with new {TAP}?tagging (tandem affinity purification)
experiments.},
doi = {10.1126/science.1087361},
pdf = {../local/Jansen2003Bayesian.pdf},
file = {Jansen2003Bayesian.pdf:local/Jansen2003Bayesian.pdf:PDF},
keywords = {biogm},
owner = {vert},
url = {http://dx.doi.org/10.1126/science.1087361}
}
@article{Majoros2005Efficient,
author = {Majoros, W. H. and Pertea, L. and Salzberg, S. L.},
title = {Efficient implementation of a generalized pair hidden {M}arkov model
for comparative gene finding.},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {1782--1788},
number = {9},
month = {May},
abstract = {M{OTIVATION}: {T}he increased availability of genome sequences of
closely related organisms has generated much interest in utilizing
homology to improve the accuracy of gene prediction programs. {G}eneralized
pair hidden {M}arkov models ({GPHMM}s) have been proposed as one
means to address this need. {H}owever, all {GPHMM} implementations
currently available are either closed-source or the details of their
operation are not fully described in the literature, leaving a significant
hurdle for others wishing to advance the state of the art in {GPHMM}
design. {RESULTS}: {W}e have developed an open-source {GPHMM} gene
finder, {TWAIN}, which performs very well on two related {A}spergillus
species, {A}.fumigatus and {A}.nidulans, finding 89\% of the exons
and predicting 74\% of the gene models exactly correctly in a test
set of 147 conserved gene pairs. {W}e describe the implementation
of this {GPHMM} and we explicitly address the assumptions and limitations
of the system. {W}e suggest possible ways of relaxing those assumptions
to improve the utility of the system without sacrificing efficiency
beyond what is practical. {AVAILABILITY}: {A}vailable at http://www.tigr.org/software/pirate/twain/twain.html
under the open-source {A}rtistic {L}icense.},
doi = {10.1093/bioinformatics/bti297},
pdf = {../local/Majoros2005Efficient.pdf},
file = {Majoros2005Efficient.pdf:local/Majoros2005Efficient.pdf:PDF},
keywords = {biogm},
owner = {vert},
pii = {bti297},
pmid = {15691859},
timestamp = {2006.01.18},
url = {http://dx.doi.org/10.1093/bioinformatics/bti297}
}
@article{McAuliffe2004Multiple-sequence,
author = {McAuliffe, J. D. and Pachter, L. and Jordan, M. I.},
title = {Multiple-sequence functional annotation and the generalized hidden
{M}arkov phylogeny.},
journal = {Bioinformatics},
year = {2004},
volume = {20},
pages = {1850--1860},
number = {12},
month = {Aug},
abstract = {M{OTIVATION}: {P}hylogenetic shadowing is a comparative genomics principle
that allows for the discovery of conserved regions in sequences from
multiple closely related organisms. {W}e develop a formal probabilistic
framework for combining phylogenetic shadowing with feature-based
functional annotation methods. {T}he resulting model, a generalized
hidden {M}arkov phylogeny ({GHMP}), applies to a variety of situations
where functional regions are to be inferred from evolutionary constraints.
{RESULTS}: {W}e show how {GHMP}s can be used to predict complete
shared gene structures in multiple primate sequences. {W}e also describe
shadower, our implementation of such a prediction system. {W}e find
that shadower outperforms previously reported ab initio gene finders,
including comparative human-mouse approaches, on a small sample of
diverse exonic regions. {F}inally, we report on an empirical analysis
of shadower's performance which reveals that as few as five well-chosen
species may suffice to attain maximal sensitivity and specificity
in exon demarcation. {AVAILABILITY}: {A} {W}eb server is available
at http://bonaire.lbl.gov/shadower},
doi = {10.1093/bioinformatics/bth153},
pdf = {../local/McAuliffe2004Multiple-sequence.pdf},
file = {McAuliffe2004Multiple-sequence.pdf:local/McAuliffe2004Multiple-sequence.pdf:PDF},
keywords = {biogm},
owner = {vert},
pii = {bth153},
pmid = {14988105},
timestamp = {2006.01.18},
url = {http://dx.doi.org/10.1093/bioinformatics/bth153}
}
@techreport{Murphy1999Modelling,
author = {Murphy, K. and Mian, S.},
title = {Modelling gene expression data using dynamic {B}ayesian networks},
institution = {Computer Science Division, University of California, Berkeley, CA.},
year = {1999},
pdf = {../local/Murphy1999Modelling.pdf},
file = {Murphy1999Modelling.pdf:local/Murphy1999Modelling.pdf:PDF},
keywords = {biogm},
owner = {vert},
timestamp = {2006.01.18}
}
@article{Segal2004module,
author = {Segal, E. and Friedman, N. and Koller, D. and Regev, A.},
title = {A module map showing conditional activity of expression modules in
cancer.},
journal = {Nat. {G}enet.},
year = {2004},
volume = {36},
pages = {1090--1098},
number = {10},
month = {Oct},
abstract = {D{NA} microarrays are widely used to study changes in gene expression
in tumors, but such studies are typically system-specific and do
not address the commonalities and variations between different types
of tumor. {H}ere we present an integrated analysis of 1,975 published
microarrays spanning 22 tumor types. {W}e describe expression profiles
in different tumors in terms of the behavior of modules, sets of
genes that act in concert to carry out a specific function. {U}sing
a simple unified analysis, we extract modules and characterize gene-expression
profiles in tumors as a combination of activated and deactivated
modules. {A}ctivation of some modules is specific to particular types
of tumor; for example, a growth-inhibitory module is specifically
repressed in acute lymphoblastic leukemias and may underlie the deregulated
proliferation in these cancers. {O}ther modules are shared across
a diverse set of clinical conditions, suggestive of common tumor
progression mechanisms. {F}or example, the bone osteoblastic module
spans a variety of tumor types and includes both secreted growth
factors and their receptors. {O}ur findings suggest that there is
a single mechanism for both primary tumor proliferation and metastasis
to bone. {O}ur analysis presents multiple research directions for
diagnostic, prognostic and therapeutic studies.},
doi = {10.1038/ng1434},
pdf = {../local/Segal2004module.pdf},
file = {Segal2004module.pdf:local/Segal2004module.pdf:PDF},
keywords = {biogm},
owner = {vert},
pii = {ng1434},
pmid = {15448693},
timestamp = {2006.01.18},
url = {http://dx.doi.org/10.1038/ng1434}
}
@article{Segal2003Module,
author = {Segal, E. and Shapira, M. and Regev, A. and Pe'er, D. and Botstein,
D. and Koller, D. and Friedman, N.},
title = {Module networks: identifying regulatory modules and their condition-specific
regulators from gene expression data.},
journal = {Nat. {G}enet.},
year = {2003},
volume = {34},
pages = {166--176},
number = {2},
month = {Jun},
abstract = {Much of a cell's activity is organized as a network of interacting
modules: sets of genes coregulated to respond to different conditions.
{W}e present a probabilistic method for identifying regulatory modules
from gene expression data. {O}ur procedure identifies modules of
coregulated genes, their regulators and the conditions under which
regulation occurs, generating testable hypotheses in the form 'regulator
{X} regulates module {Y} under conditions {W}'. {W}e applied the
method to a {S}accharomyces cerevisiae expression data set, showing
its ability to identify functionally coherent modules and their correct
regulators. {W}e present microarray experiments supporting three
novel predictions, suggesting regulatory roles for previously uncharacterized
proteins.},
doi = {10.1038/ng1165},
pdf = {../local/Segal2003Module.pdf},
file = {Segal2003Module.pdf:Segal2003Module.pdf:PDF},
keywords = {biogm},
owner = {vert},
pii = {ng1165},
pmid = {12740579},
timestamp = {2006.01.18},
url = {http://dx.doi.org/10.1038/ng1165}
}
@article{Xing2004LOGOS,
author = {Xing, E. P. and Wu, W. and Jordan, M. I. and Karp, R. M.},
title = {L{OGOS}: {A} modular {B}ayesian model for de novo motif detection},
journal = {J. {B}ioinform. {C}omput. {B}iol.},
year = {2004},
volume = {2},
pages = {127--154},
abstract = {The complexity of the global organization and internal structure of
motifs in higher eukaryotic organisms raises significant challenges
for motif detection techniques. {T}o achieve successful de novo motif
detection, it is necessary to model the complex dependencies within
and among motifs and to incorporate biological prior knowledge. {I}n
this paper, we present {LOGOS}, an integrated {LO}cal and {G}l{O}bal
motif {S}equence model for biopolymer sequences, which provides a
principled framework for developing, modularizing, extending and
computing expressive motif models for complex biopolymer sequence
analysis. {LOGOS} consists of two interacting submodels: {HMDM},
a local alignment model capturing biological prior knowledge and
positional dependency within the motif local structure; and {HMM},
a global motif distribution model modeling frequencies and dependencies
of motif occurrences. {M}odel parameters can be fit using training
motifs within an empirical {B}ayesian framework. {A} variational
{EM} algorithm is developed for de novo motif detection. {LOGOS}
improves over existing models that ignore biological priors and dependencies
in motif structures and motif occurrences, and demonstrates superior
performance on both semi-realistic test data and cis-regulatory sequences
from yeast and {D}rosophila genomes with regard to sensitivity, specificity,
flexibility and extensibility.},
doi = {10.1142/S0219720004000508},
pdf = {../local/Xing2004LOGOS.pdf},
file = {Xing2004LOGOS.pdf:Xing2004LOGOS.pdf:PDF},
keywords = {biogm},
owner = {vert},
timestamp = {2006.01.18},
url = {http://dx.doi.org/10.1142/S0219720004000508}
}
@comment{{jabref-meta: selector_author:}}
@comment{{jabref-meta: selector_journal:Adv. Drug Deliv. Rev.;Am. J. Hu
m. Genet.;Am. J. Pathol.;Ann. Appl. Stat.;Ann. Math. Statist.;Ann. N.
Y. Acad. Sci.;Ann. Probab.;Ann. Stat.;Artif. Intell. Med.;Bernoulli;Bi
ochim. Biophys. Acta;Bioinformatics;Biometrika;BMC Bioinformatics;Br.
J. Pharmacol.;Breast Cancer Res.;Cell;Cell. Signal.;Chem. Res. Toxicol
.;Clin. Cancer Res.;Combinator. Probab. Comput.;Comm. Pure Appl. Math.
;Comput. Chem.;Comput. Comm. Rev.;Comput. Stat. Data An.;Curr. Genom.;
Curr. Opin. Chem. Biol.;Curr. Opin. Drug Discov. Devel.;Data Min. Know
l. Discov.;Electron. J. Statist.;Eur. J. Hum. Genet.;FEBS Lett.;Found.
Comput. Math.;Genome Biol.;IEEE T. Neural Networ.;IEEE T. Pattern. An
al.;IEEE T. Signal. Proces.;IEEE Trans. Inform. Theory;IEEE Trans. Kno
wl. Data Eng.;IEEE/ACM Trans. Comput. Biol. Bioinf.;Int. J. Comput. Vi
sion;Int. J. Data Min. Bioinform.;Int. J. Qantum Chem.;J Biol Syst;J.
ACM;J. Am. Soc. Inf. Sci. Technol.;J. Am. Stat. Assoc.;J. Bioinform. C
omput. Biol.;J. Biol. Chem.;J. Biomed. Inform.;J. Cell. Biochem.;J. Ch
em. Inf. Comput. Sci.;J. Chem. Inf. Model.;J. Clin. Oncol.;J. Comput.
Biol.;J. Comput. Graph. Stat.;J. Eur. Math. Soc.;J. Intell. Inform. Sy
st.;J. Mach. Learn. Res.;J. Med. Chem.;J. Mol. BIol.;J. R. Stat. Soc.
Ser. B;Journal of Statistical Planning and Inference;Mach. Learn.;Math
. Program.;Meth. Enzymol.;Mol. Biol. Cell;Mol. Biol. Evol.;Mol. Cell.
Biol.;Mol. Syst. Biol.;N. Engl. J. Med.;Nat. Biotechnol.;Nat. Genet.;N
at. Med.;Nat. Methods;Nat. Rev. Cancer;Nat. Rev. Drug Discov.;Nat. Rev
. Genet.;Nature;Neural Comput.;Neural Network.;Neurocomputing;Nucleic
Acids Res.;Pattern Anal. Appl.;Pattern Recognit.;Phys. Rev. E;Phys. Re
v. Lett.;PLoS Biology;PLoS Comput. Biol.;Probab. Theory Relat. Fields;
Proc. IEEE;Proc. Natl. Acad. Sci. USA;Protein Eng.;Protein Eng. Des. S
el.;Protein Sci.;Protein. Struct. Funct. Genet.;Random Struct. Algorit
hm.;Rev. Mod. Phys.;Science;Stat. Probab. Lett.;Statistica Sinica;Theo
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