907 research outputs found
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
Pairwise gene GO-based measures for biclustering of high-dimensional expression data
Background: Biclustering algorithms search for groups of genes that share the same
behavior under a subset of samples in gene expression data. Nowadays, the biological
knowledge available in public repositories can be used to drive these algorithms to
find biclusters composed of groups of genes functionally coherent. On the other hand,
a distance among genes can be defined according to their information stored in Gene
Ontology (GO). Gene pairwise GO semantic similarity measures report a value for each
pair of genes which establishes their functional similarity. A scatter search-based
algorithm that optimizes a merit function that integrates GO information is studied in
this paper. This merit function uses a term that addresses the information through a GO
measure.
Results: The effect of two possible different gene pairwise GO measures on the
performance of the algorithm is analyzed. Firstly, three well known yeast datasets with
approximately one thousand of genes are studied. Secondly, a group of human
datasets related to clinical data of cancer is also explored by the algorithm. Most of
these data are high-dimensional datasets composed of a huge number of genes. The
resultant biclusters reveal groups of genes linked by a same functionality when the
search procedure is driven by one of the proposed GO measures. Furthermore, a
qualitative biological study of a group of biclusters show their relevance from a cancer
disease perspective.
Conclusions: It can be concluded that the integration of biological information
improves the performance of the biclustering process. The two different GO measures
studied show an improvement in the results obtained for the yeast dataset. However, if
datasets are composed of a huge number of genes, only one of them really improves
the algorithm performance. This second case constitutes a clear option to explore
interesting datasets from a clinical point of view.Ministerio de Economía y Competitividad TIN2014-55894-C2-
Techniques for clustering gene expression data
Many clustering techniques have been proposed for the analysis of gene expression data obtained from microarray experiments. However, choice of suitable method(s) for a given experimental dataset is not straightforward. Common approaches do not translate well and fail to take account of the data profile. This review paper surveys state of the art applications which recognises these limitations and implements procedures to overcome them. It provides a framework for the evaluation of clustering in gene expression analyses. The nature of microarray data is discussed briefly. Selected examples are presented for the clustering methods considered
Profile Likelihood Biclustering
Biclustering, the process of simultaneously clustering the rows and columns
of a data matrix, is a popular and effective tool for finding structure in a
high-dimensional dataset. Many biclustering procedures appear to work well in
practice, but most do not have associated consistency guarantees. To address
this shortcoming, we propose a new biclustering procedure based on profile
likelihood. The procedure applies to a broad range of data modalities,
including binary, count, and continuous observations. We prove that the
procedure recovers the true row and column classes when the dimensions of the
data matrix tend to infinity, even if the functional form of the data
distribution is misspecified. The procedure requires computing a combinatorial
search, which can be expensive in practice. Rather than performing this search
directly, we propose a new heuristic optimization procedure based on the
Kernighan-Lin heuristic, which has nice computational properties and performs
well in simulations. We demonstrate our procedure with applications to
congressional voting records, and microarray analysis.Comment: 40 pages, 11 figures; R package in development at
https://github.com/patperry/biclustp
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