274 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
Binary Particle Swarm Optimization based Biclustering of Web usage Data
Web mining is the nontrivial process to discover valid, novel, potentially
useful knowledge from web data using the data mining techniques or methods. It
may give information that is useful for improving the services offered by web
portals and information access and retrieval tools. With the rapid development
of biclustering, more researchers have applied the biclustering technique to
different fields in recent years. When biclustering approach is applied to the
web usage data it automatically captures the hidden browsing patterns from it
in the form of biclusters. In this work, swarm intelligent technique is
combined with biclustering approach to propose an algorithm called Binary
Particle Swarm Optimization (BPSO) based Biclustering for Web Usage Data. The
main objective of this algorithm is to retrieve the global optimal bicluster
from the web usage data. These biclusters contain relationships between web
users and web pages which are useful for the E-Commerce applications like web
advertising and marketing. Experiments are conducted on real dataset to prove
the efficiency of the proposed algorithms
Correlation–Based Scatter Search for Discovering Biclusters from Gene Expression Data
Scatter Search is an evolutionary method that combines ex isting solutions to create new offspring as the well–known genetic algo rithms. This paper presents a Scatter Search with the aim of finding
biclusters from gene expression data. However, biclusters with certain
patterns are more interesting from a biological point of view. Therefore,
the proposed Scatter Search uses a measure based on linear correlations
among genes to evaluate the quality of biclusters. As it is usual in Scatter
Search methodology an improvement method is included which avoids
to find biclusters with negatively correlated genes. Experimental results
from yeast cell cycle and human B-cell lymphoma datasets are reported
showing a remarkable performance of the proposed method and measureMinisterio de Ciencia y Tecnología TIN2007-68084-C00Junta de Andalucía P07-TIC-0261
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-
Discernment of possible mechanisms of hepatotoxicity via biological processes over-represented by co-expressed genes
<p>Abstract</p> <p>Background</p> <p>Hepatotoxicity is a form of liver injury caused by exposure to stressors. Genomic-based approaches have been used to detect changes in transcription in response to hepatotoxicants. However, there are no straightforward ways of using co-expressed genes anchored to a phenotype or constrained by the experimental design for discerning mechanisms of a biological response.</p> <p>Results</p> <p>Through the analysis of a gene expression dataset containing 318 liver samples from rats exposed to hepatotoxicants and leveraging alanine aminotransferase (ALT), a serum enzyme indicative of liver injury as the phenotypic marker, we identified biological processes and molecular pathways that may be associated with mechanisms of hepatotoxicity. Our analysis used an approach called Coherent Co-expression Biclustering (cc-Biclustering) for clustering a subset of genes through a coherent (consistency) measure within each group of samples representing a subset of experimental conditions. Supervised biclustering identified 87 genes co-expressed and correlated with ALT in all the samples exposed to the chemicals. None of the over-represented pathways related to liver injury. However, biclusters with subsets of samples exposed to one of the 7 hepatotoxicants, but not to a non-toxic isomer, contained co-expressed genes that represented pathways related to a stress response. Unsupervised biclustering of the data resulted in 1) four to five times more genes within the bicluster containing all the samples exposed to the chemicals, 2) biclusters with co-expression of genes that discerned 1,4 dichlorobenzene (a non-toxic isomer at low and mid doses) from the other chemicals, pathways and biological processes that underlie liver injury and 3) a bicluster with genes up-regulated in an early response to toxic exposure.</p> <p>Conclusion</p> <p>We obtained clusters of co-expressed genes that over-represented biological processes and molecular pathways related to hepatotoxicity in the rat. The mechanisms involved in the response of the liver to the exposure to 1,4-dichlorobenzene suggest non-genotoxicity whereas the exposure to the hepatotoxicants could be DNA damaging leading to overall genomic instability and activation of cell cycle check point signaling. In addition, key pathways and biological processes representative of an inflammatory response, energy production and apoptosis were impacted by the hepatotoxicant exposures that manifested liver injury in the rat.</p
Biclustering of Gene Expression Data by Correlation-Based Scatter Search
BACKGROUND: The analysis of data generated by microarray technology is very useful to understand how the genetic information becomes functional gene products. Biclustering algorithms can determine a group of genes which are co-expressed under a set of experimental conditions. Recently, new biclustering methods based on metaheuristics have been proposed. Most of them use the Mean Squared Residue as merit function but interesting and relevant patterns from a biological point of view such as shifting and scaling patterns may not be detected using this measure. However, it is important to discover this type of patterns since commonly the genes can present a similar behavior although their expression levels vary in different ranges or magnitudes. METHODS: Scatter Search is an evolutionary technique that is based on the evolution of a small set of solutions which are chosen according to quality and diversity criteria. This paper presents a Scatter Search with the aim of finding biclusters from gene expression data. In this algorithm the proposed fitness function is based on the linear correlation among genes to detect shifting and scaling patterns from genes and an improvement method is included in order to select just positively correlated genes. RESULTS: The proposed algorithm has been tested with three real data sets such as Yeast Cell Cycle dataset, human B-cells lymphoma dataset and Yeast Stress dataset, finding a remarkable number of biclusters with shifting and scaling patterns. In addition, the performance of the proposed method and fitness function are compared to that of CC, OPSM, ISA, BiMax, xMotifs and Samba using Gene the Ontology Database
Biclustering on expression data: A review
Biclustering has become a popular technique for the study of gene expression data, especially for discovering functionally related gene sets under different subsets of experimental conditions. Most of biclustering approaches use a measure or cost function that determines the quality of biclusters. In such cases, the development of both a suitable heuristics and a good measure for guiding the search are essential for discovering interesting biclusters in an expression matrix. Nevertheless, not all existing biclustering approaches base their search on evaluation measures for biclusters. There exists a diverse set of biclustering tools that follow different strategies and algorithmic concepts which guide the search towards meaningful results. In this paper we present a extensive survey of biclustering approaches, classifying them into two categories according to whether or not use evaluation metrics within the search method: biclustering algorithms based on evaluation measures and non metric-based biclustering algorithms. In both cases, they have been classified according to the type of meta-heuristics which they are based on.Ministerio de Economía y Competitividad TIN2011-2895
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