48,775 research outputs found
Boosting the concordance index for survival data - a unified framework to derive and evaluate biomarker combinations
The development of molecular signatures for the prediction of time-to-event
outcomes is a methodologically challenging task in bioinformatics and
biostatistics. Although there are numerous approaches for the derivation of
marker combinations and their evaluation, the underlying methodology often
suffers from the problem that different optimization criteria are mixed during
the feature selection, estimation and evaluation steps. This might result in
marker combinations that are only suboptimal regarding the evaluation criterion
of interest. To address this issue, we propose a unified framework to derive
and evaluate biomarker combinations. Our approach is based on the concordance
index for time-to-event data, which is a non-parametric measure to quantify the
discrimatory power of a prediction rule. Specifically, we propose a
component-wise boosting algorithm that results in linear biomarker combinations
that are optimal with respect to a smoothed version of the concordance index.
We investigate the performance of our algorithm in a large-scale simulation
study and in two molecular data sets for the prediction of survival in breast
cancer patients. Our numerical results show that the new approach is not only
methodologically sound but can also lead to a higher discriminatory power than
traditional approaches for the derivation of gene signatures.Comment: revised manuscript - added simulation study, additional result
Bayesian hierarchical clustering for studying cancer gene expression data with unknown statistics
Clustering analysis is an important tool in studying gene expression data. The Bayesian hierarchical clustering (BHC) algorithm can automatically infer the number of clusters and uses Bayesian model selection to improve clustering quality. In this paper, we present an extension of the BHC algorithm. Our Gaussian BHC (GBHC) algorithm represents data as a mixture of Gaussian distributions. It uses normal-gamma distribution as a conjugate prior on the mean and precision of each of the Gaussian components. We tested GBHC over 11 cancer and 3 synthetic datasets. The results on cancer datasets show that in sample clustering, GBHC on average produces a clustering partition that is more concordant with the ground truth than those obtained from other commonly used algorithms. Furthermore, GBHC frequently infers the number of clusters that is often close to the ground truth. In gene clustering, GBHC also produces a clustering partition that is more biologically plausible than several other state-of-the-art methods. This suggests GBHC as an alternative tool for studying gene expression data. The implementation of GBHC is available at https://sites.
google.com/site/gaussianbhc
Generalized power method for sparse principal component analysis
In this paper we develop a new approach to sparse principal component
analysis (sparse PCA). We propose two single-unit and two block optimization
formulations of the sparse PCA problem, aimed at extracting a single sparse
dominant principal component of a data matrix, or more components at once,
respectively. While the initial formulations involve nonconvex functions, and
are therefore computationally intractable, we rewrite them into the form of an
optimization program involving maximization of a convex function on a compact
set. The dimension of the search space is decreased enormously if the data
matrix has many more columns (variables) than rows. We then propose and analyze
a simple gradient method suited for the task. It appears that our algorithm has
best convergence properties in the case when either the objective function or
the feasible set are strongly convex, which is the case with our single-unit
formulations and can be enforced in the block case. Finally, we demonstrate
numerically on a set of random and gene expression test problems that our
approach outperforms existing algorithms both in quality of the obtained
solution and in computational speed.Comment: Submitte
SUBIC: A Supervised Bi-Clustering Approach for Precision Medicine
Traditional medicine typically applies one-size-fits-all treatment for the
entire patient population whereas precision medicine develops tailored
treatment schemes for different patient subgroups. The fact that some factors
may be more significant for a specific patient subgroup motivates clinicians
and medical researchers to develop new approaches to subgroup detection and
analysis, which is an effective strategy to personalize treatment. In this
study, we propose a novel patient subgroup detection method, called Supervised
Biclustring (SUBIC) using convex optimization and apply our approach to detect
patient subgroups and prioritize risk factors for hypertension (HTN) in a
vulnerable demographic subgroup (African-American). Our approach not only finds
patient subgroups with guidance of a clinically relevant target variable but
also identifies and prioritizes risk factors by pursuing sparsity of the input
variables and encouraging similarity among the input variables and between the
input and target variable
Challenges of Big Data Analysis
Big Data bring new opportunities to modern society and challenges to data
scientists. On one hand, Big Data hold great promises for discovering subtle
population patterns and heterogeneities that are not possible with small-scale
data. On the other hand, the massive sample size and high dimensionality of Big
Data introduce unique computational and statistical challenges, including
scalability and storage bottleneck, noise accumulation, spurious correlation,
incidental endogeneity, and measurement errors. These challenges are
distinguished and require new computational and statistical paradigm. This
article give overviews on the salient features of Big Data and how these
features impact on paradigm change on statistical and computational methods as
well as computing architectures. We also provide various new perspectives on
the Big Data analysis and computation. In particular, we emphasis on the
viability of the sparsest solution in high-confidence set and point out that
exogeneous assumptions in most statistical methods for Big Data can not be
validated due to incidental endogeneity. They can lead to wrong statistical
inferences and consequently wrong scientific conclusions
Unconventional machine learning of genome-wide human cancer data
Recent advances in high-throughput genomic technologies coupled with
exponential increases in computer processing and memory have allowed us to
interrogate the complex aberrant molecular underpinnings of human disease from
a genome-wide perspective. While the deluge of genomic information is expected
to increase, a bottleneck in conventional high-performance computing is rapidly
approaching. Inspired in part by recent advances in physical quantum
processors, we evaluated several unconventional machine learning (ML)
strategies on actual human tumor data. Here we show for the first time the
efficacy of multiple annealing-based ML algorithms for classification of
high-dimensional, multi-omics human cancer data from the Cancer Genome Atlas.
To assess algorithm performance, we compared these classifiers to a variety of
standard ML methods. Our results indicate the feasibility of using
annealing-based ML to provide competitive classification of human cancer types
and associated molecular subtypes and superior performance with smaller
training datasets, thus providing compelling empirical evidence for the
potential future application of unconventional computing architectures in the
biomedical sciences
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