47,565 research outputs found
An integrative analysis of cancer gene expression studies using Bayesian latent factor modeling
We present an applied study in cancer genomics for integrating data and
inferences from laboratory experiments on cancer cell lines with observational
data obtained from human breast cancer studies. The biological focus is on
improving understanding of transcriptional responses of tumors to changes in
the pH level of the cellular microenvironment. The statistical focus is on
connecting experimentally defined biomarkers of such responses to clinical
outcome in observational studies of breast cancer patients. Our analysis
exemplifies a general strategy for accomplishing this kind of integration
across contexts. The statistical methodologies employed here draw heavily on
Bayesian sparse factor models for identifying, modularizing and correlating
with clinical outcome these signatures of aggregate changes in gene expression.
By projecting patterns of biological response linked to specific experimental
interventions into observational studies where such responses may be evidenced
via variation in gene expression across samples, we are able to define
biomarkers of clinically relevant physiological states and outcomes that are
rooted in the biology of the original experiment. Through this approach we
identify microenvironment-related prognostic factors capable of predicting long
term survival in two independent breast cancer datasets. These results suggest
possible directions for future laboratory studies, as well as indicate the
potential for therapeutic advances though targeted disruption of specific
pathway components.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS261 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Getting started in probabilistic graphical models
Probabilistic graphical models (PGMs) have become a popular tool for
computational analysis of biological data in a variety of domains. But, what
exactly are they and how do they work? How can we use PGMs to discover patterns
that are biologically relevant? And to what extent can PGMs help us formulate
new hypotheses that are testable at the bench? This note sketches out some
answers and illustrates the main ideas behind the statistical approach to
biological pattern discovery.Comment: 12 pages, 1 figur
Machine Learning and Integrative Analysis of Biomedical Big Data.
Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues
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