Comparative Evaluation of Set-Level Techniques in Microarray Classification

Kléma, Jǐŕı; Holec, Matĕj; Železný, Filip; Tolar, Jakub

doi:10.1007/978-3-642-21260-4_27

Cited by 4 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other pieces of literature, involving set-level techniques, include [41] and [42]. The use of differentially expressed genes is also being currently investigated by us.…”

Section: Discussionmentioning

confidence: 99%

Feature Selection and Clustering of Gene Expression Profiles Using Biological Knowledge

Ghosh

2012

IEEE Trans. Syst., Man, Cybern. C

View full text Add to dashboard Cite

In this paper, a novel feature selection algorithm, which is governed by biological knowledge, is developed. Gene expression data being high dimensional and redundant, dimensionality reduction is of prime concern. We employ the algorithm clustering large applications based on RAN-domized search (CLARANS) for attribute clustering and dimensionality reduction based on gene ontology (GO) study. Feature selection with unsupervised learning is a difficult problem, with neither class labels present nor any guidance available to the search. Determination of the optimal number of clusters is another major issue, and has an impact on the resulting output. The use of GO analysis helps in the automated selection of biologically meaningful partitions. Tools such as Eisen plot and cluster profiles of these clusters help establish their coherence. Important representative features (or genes) are extracted from each correlated set of genes in such partitions. The algorithm is implemented on high-dimensional Yeast cell-cycle, Human Multiple Tissues, and Leukemia microarray data. In the second pass, clustering on the reduced gene space validates preservation of the inherent behavior of the original high-dimensional expression profiles. While the reduced gene set forms a biologically meaningful gene space, it simultaneously leads to a decrease in computational burden. External validation of the reduced subspace, using various well-known classifiers, establishes the effectiveness of the proposed methodology.Index Terms-Attribute clustering, clustering large applications based on RAN-domized search (CLARANS), feature selection, gene ontology (GO) medoid.

show abstract

“…Other pieces of literature, involving set-level techniques, include [41] and [42]. The use of differentially expressed genes is also being currently investigated by us.…”

Section: Discussionmentioning

confidence: 99%

Feature Selection and Clustering of Gene Expression Profiles Using Biological Knowledge

Ghosh

2012

IEEE Trans. Syst., Man, Cybern. C

View full text Add to dashboard Cite

show abstract

“…Many approaches are currently available that focus on the analysis of gene sets including Gene Set Enrichment Analysis [21] and Gene Set Analysis [7]. Compared to lists of differentially expressed genes, gene sets are more interpretable as they correspond to lists of biological processes [15] and may be thought of as derived sample features that succinctly summarize the original gene expression data [10]. Furthermore, by using gene sets, data sets from multiple platforms may be integrated [10].…”

Section: Introductionmentioning

confidence: 99%

Gene Set Cultural Algorithm

Judeh

Jayyousi

Acharya³

et al. 2013

Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics

View full text Add to dashboard Cite

With the increasing availability of gene sets, novel approaches that focus on reconstructing networks from gene sets are of interest. Currently, few computational approaches explore the search space of candidate networks using a parallel search. As such, novel methods that employ search agents are needed to help better escape local optima. In particular, gene sets may model signal transduction events, which refer to linear chains or cascades of reactions starting at the cell membrane and ending at the cell nucleus. These events may be indirectly observed as a set of unordered and overlapping gene sets. Thus, the underlying goal is to reverse engineer the order information within each gene set to reconstruct the underlying source network. To achieve this goal, we developed the Gene Set Cultural Algorithm to discover the true order of the gene sets and to reconstruct the underlying network. In a proof of concept study, we show that the Gene Set Cultural Algorithm can satisfactorily reconstruct three E. coli networks from the DREAM initiative using simulated and unordered gene sets as the input.

show abstract

“…Gene sets are more interpretable as they correspond to lists of biological processes [15] and may be thought of as derived sample features that succinctly summarize the original gene expression data [16]. Furthermore, by using gene sets, data sets from multiple platforms may be integrated [16].…”

Section: Introductionmentioning

confidence: 99%

GSCA: Reconstructing biological pathway topologies using a cultural algorithms approach

Judeh

Jayyousi

Acharya³

et al. 2014

2014 IEEE Congress on Evolutionary Computation (CEC)

View full text Add to dashboard Cite

With the increasing availability of gene sets and pathway resources, novel approaches that combine both resources to reconstruct networks from gene sets are of interest. Currently, few computational approaches explore the search space of candidate networks using a parallel search. In particular, search agents employed by evolutionary computational approaches may better escape false peaks compared to previous approaches. It may also be hypothesized that gene sets may model signal transduction events, which refer to linear chains or cascades of reactions starting at the cell membrane and ending at the cell nucleus. These events may be indirectly observed as a set of unordered and overlapping gene sets. Thus, the goal is to reverse engineer the order information within each gene set to reconstruct the underlying source network using prior knowledge to limit the search space.We propose the Gene Set Cultural Algorithm (GSCA) to reconstruct networks from unordered gene sets. We introduce a robust heuristic based on the arborescence of a directed graph that performs well for random topological sort orderings across gene sets simulated for four E. coli networks and five Insilico networks from the DREAM3 and DREAM4 initiatives, respectively. Furthermore, GSCA performs favorably when reconstructing networks from randomly ordered gene sets for the aforementioned networks. Finally, we note that from a set of 23 gene sets discretized from a set of 300 S. cerevisiae expression profiles, GSCA reconstructs a network preserving most of the weak order information found in the KEGG Cell Cycle pathway, which was used as prior knowledge.

show abstract

Comparative Evaluation of Set-Level Techniques in Microarray Classification

Cited by 4 publications

References 22 publications

Feature Selection and Clustering of Gene Expression Profiles Using Biological Knowledge

Feature Selection and Clustering of Gene Expression Profiles Using Biological Knowledge

Gene Set Cultural Algorithm

GSCA: Reconstructing biological pathway topologies using a cultural algorithms approach

Contact Info

Product

Resources

About