16,891 research outputs found
Analysing multiple types of molecular profiles simultaneously: connecting the needles in the haystack
It has been shown that a random-effects framework can be used to test the
association between a gene's expression level and the number of DNA copies of a
set of genes. This gene-set modelling framework was later applied to find
associations between mRNA expression and microRNA expression, by defining the
gene sets using target prediction information.
Here, we extend the model introduced by Menezes et al (2009) to consider the
effect of not just copy number, but also of other molecular profiles such as
methylation changes and loss-of-heterozigosity (LOH), on gene expression
levels. We will consider again sets of measurements, to improve robustness of
results and increase the power to find associations. Our approach can be used
genome-wide to find associations, yields a test to help separate true
associations from noise and can include confounders.
We apply our method to colon and to breast cancer samples, for which
genome-wide copy number, methylation and gene expression profiles are
available. Our findings include interesting gene expression-regulating
mechanisms, which may involve only one of copy number or methylation, or both
for the same samples. We even are able to find effects due to different
molecular mechanisms in different samples.
Our method can equally well be applied to cases where other types of
molecular (high-dimensional) data are collected, such as LOH, SNP genotype and
microRNA expression data. Computationally efficient, it represents a flexible
and powerful tool to study associations between high-dimensional datasets. The
method is freely available via the SIM BioConductor package
An Efficient Algorithm for Upper Bound on the Partition Function of Nucleic Acids
It has been shown that minimum free energy structure for RNAs and RNA-RNA
interaction is often incorrect due to inaccuracies in the energy parameters and
inherent limitations of the energy model. In contrast, ensemble based
quantities such as melting temperature and equilibrium concentrations can be
more reliably predicted. Even structure prediction by sampling from the
ensemble and clustering those structures by Sfold [7] has proven to be more
reliable than minimum free energy structure prediction. The main obstacle for
ensemble based approaches is the computational complexity of the partition
function and base pairing probabilities. For instance, the space complexity of
the partition function for RNA-RNA interaction is and the time
complexity is which are prohibitively large [4,12]. Our goal in this
paper is to give a fast algorithm, based on sparse folding, to calculate an
upper bound on the partition function. Our work is based on the recent
algorithm of Hazan and Jaakkola [10]. The space complexity of our algorithm is
the same as that of sparse folding algorithms, and the time complexity of our
algorithm is for single RNA and for RNA-RNA
interaction in practice, in which is the running time of sparse folding
and () is a sequence dependent parameter
Parallel implementation of stochastic simulation for large-scale cellular processes
Experimental and theoretical studies have shown the importance of stochastic processes in genetic regulatory networks and cellular processes. Cellular networks and genetic circuits often involve small numbers of key proteins such as transcriptional factors and signaling proteins. In recent years stochastic models have been used successfully for studying noise in biological pathways, and stochastic modelling of biological systems has become a very important research field in computational biology. One of the challenge problems in this field is the reduction of the huge computing time in stochastic simulations. Based on the system of the mitogen-activated protein kinase cascade that is activated by epidermal growth factor, this work give a parallel implementation by using OpenMP and parallelism across the simulation. Special attention is paid to the independence of the generated random numbers in parallel computing, that is a key criterion for the success of stochastic simulations. Numerical results indicate that parallel computers can be used as an efficient tool for simulating the dynamics of large-scale genetic regulatory networks and cellular processes
Tellipsoid: Exploiting inter-gene correlation for improved detection of differential gene expression
Motivation: Algorithms for differential analysis of microarray data are vital
to modern biomedical research. Their accuracy strongly depends on effective
treatment of inter-gene correlation. Correlation is ordinarily accounted for in
terms of its effect on significance cut-offs. In this paper it is shown that
correlation can, in fact, be exploited {to share information across tests},
which, in turn, can increase statistical power.
Results: Vastly and demonstrably improved differential analysis approaches
are the result of combining identifiability (the fact that in most microarray
data sets, a large proportion of genes can be identified a priori as
non-differential) with optimization criteria that incorporate correlation. As a
special case, we develop a method which builds upon the widely used two-sample
t-statistic based approach and uses the Mahalanobis distance as an optimality
criterion. Results on the prostate cancer data of Singh et al. (2002) suggest
that the proposed method outperforms all published approaches in terms of
statistical power.
Availability: The proposed algorithm is implemented in MATLAB and in R. The
software, called Tellipsoid, and relevant data sets are available at
http://www.egr.msu.edu/~desaikeyComment: 19 pages, Submitted to Bioinformatic
Processing of Electronic Health Records using Deep Learning: A review
Availability of large amount of clinical data is opening up new research
avenues in a number of fields. An exciting field in this respect is healthcare,
where secondary use of healthcare data is beginning to revolutionize
healthcare. Except for availability of Big Data, both medical data from
healthcare institutions (such as EMR data) and data generated from health and
wellbeing devices (such as personal trackers), a significant contribution to
this trend is also being made by recent advances on machine learning,
specifically deep learning algorithms
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