Clustering of gene expression data using a local shape-based similarity measure

Balasubramaniyan, Rajarajeswari; Hüllermeier, Eyke; Weskamp, Nils; Kämper, Jörg

doi:10.1093/bioinformatics/bti095

Cited by 101 publications

(73 citation statements)

References 25 publications

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“…Instead, robust, coherent, and dominating qualitative features and similarities could be a more informative proxy for the information content of the expression experiment. The raw data are transformed to sequences of events or symbols, and these are further analyzed for consistencies, either local or global (56). Looking for general shapes as opposed to quantifying distances allows for, among other things, a more flexible representation, which uncovers more intricate relations among expression profiles, such as time shifts and inversion in expression profiles (57).…”

Section: Feature-based Clustering Methodsmentioning

confidence: 99%

Analysis of Time-Series Gene Expression Data: Methods, Challenges, and Opportunities

Androulakis

Yang

Almon

2007

Annu. Rev. Biomed. Eng.

121

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Monitoring the change in expression patterns over time provides the distinct possibility of unraveling the mechanistic drivers characterizing cellular responses. Gene arrays measuring the level of mRNA expression of thousands of genes simultaneously provide a method of highthroughput data collection necessary for obtaining the scope of data required for understanding the complexities of living organisms. Unraveling the coherent complex structures of transcriptional dynamics is the goal of a large family of computational methods aiming at upgrading the information content of time-course gene expression data. In this review, we summarize the qualitative characteristics of these approaches, discuss the main challenges that this type of complex data present, and, finally, explore the opportunities in the context of developing mechanistic models of cellular response. Keywords microarrays; bioinformatics; regulation; clustering; pharmacogenomics TEMPORAL GENE EXPRESSION ANALYSISAt any given time a cell will only express a small fraction of the thousands of genes in the organism's genome. Expressed genes reflect the structure and functional capacities of the cell as well as the ability of the cell to respond to external stimuli. In a complex organism, external stimuli to a great extent take the form of chemical messages whose purpose is to coordinate the function of the complex society of cells (1). Gene arrays, which measure the level of mRNA expression of thousands of genes simultaneously, provide a method of highthroughput data collection necessary for obtaining the scope of data required for understanding the complexities of living organisms. Monitoring the change in expression patterns over time using gene arrays provides an approach for capturing the multidimensional dynamics of complex biological systems. By using gene arrays in a time series paradigm, we are able to observe the emergence of coherent temporal responses of many interacting components. The data should provide the basis for understanding evolving Copyright © 2007 Global gene expression analysis has been celebrated as a major revolution in modern biology (2). The ability to monitor simultaneously the expression of the genes composing the entire genome has generated unimaginable possibilities (3-5). Despite some criticism regarding the cross-platform reproducibility of expression experiments (6, 7), more recent evidence (8, 9) supports the informative nature of the experiment and the importance of the approach (10). Microarray analysis has found widespread applications from characterizing terminal states, i.e., benign versus malignant tumors (11), to attempts to decipher the evolution of complex diseases and cell fates (12-16). Hence, the nature of the data broadly defines the nature of the problems to be addressed.Boundary problems use the expression measurements as feature vectors that characterize static points in multidimensional spaces. Therefore, multiple samples, for example, from the same tissue of different patients (diseased/nondi...

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Section: Feature-based Clustering Methodsmentioning

confidence: 99%

Analysis of Time-Series Gene Expression Data: Methods, Challenges, and Opportunities

Androulakis

Yang

Almon

2007

Annu. Rev. Biomed. Eng.

121

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“…For example, it is relevant for the prediction of every sort of ordering of a fixed set of elements, such as the preferential order of a fixed set of products (e.g., different types of holiday apartments) based on demographic properties of a person, or the ordering of a set of genes according to their expression level (as measured by microarray analysis) based on features of their phylogenetic profile [1]. Another application scenario is meta-learning, where the task is to rank learning algorithms according to their suitability for a new dataset, based on the characteristics of this dataset [7].…”

Section: Label Rankingmentioning

confidence: 99%

Preference Learning: An Introduction

Fürnkranz¹,

Hüllermeier

2010

Preference Learning

106

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Abstract. This introduction gives a brief overview of the field of preference learning and, along the way, tries to establish a unified terminology. Special emphasis will be put on learning to rank, which is by now one of the most extensively studied problem tasks in preference learning and also prominently represented in this book. We propose a categorization of ranking problems into object ranking, instance ranking, and label ranking. Moreover, we introduce these scenarios in a formal way, discuss different ways in which the learning of ranking functions can be approached, and explain how the contributions collected in this book relate to this categorization. Finally, we also highlight some important applications of preference learning methods.

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“…Rank correlation has been employed on an eclectic variety of domains, including bioinformatics (Balasubramaniyan et al, 2005), information retrieval (Yilmaz et al, 2008), recommender systems (Breese et al, 1998), and determining molecular structure by lanthanide shift reagents (Li and Lee, 1980). Finding coherent subgroups of the dataset at hand displaying exceptional interaction between two targets, as measured through rank correlation, should be interesting to practitioners in these fields.…”

Section: Introductionmentioning

confidence: 99%

Exceptionally Monotone Models -- The Rank Correlation Model Class for Exceptional Model Mining

Downar

Duivesteijn

2015

2015 IEEE International Conference on Data Mining

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Abstract. Exceptional Model Mining strives to find coherent subgroups of the dataset where multiple target attributes interact in an unusual way. One instance of such an investigated form of interaction is Pearson's correlation coefficient between two targets. EMM then finds subgroups with an exceptionally linear relation between the targets. In this paper, we enrich the EMM toolbox by developing the more general rank correlation model class. We find subgroups with an exceptionally monotone relation between the targets. Apart from catering for this richer set of relations, the rank correlation model class does not necessarily require the assumption of target normality, which is implicitly invoked in the Pearson's correlation model class. Furthermore, it is less sensitive to outliers. We provide pseudocode for the employed algorithm and analyze its computational complexity, and experimentally illustrate what the rank correlation model class for EMM can find for you on six datasets from an eclectic variety of domains.

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Clustering of gene expression data using a local shape-based similarity measure

Cited by 101 publications

References 25 publications

Analysis of Time-Series Gene Expression Data: Methods, Challenges, and Opportunities

Analysis of Time-Series Gene Expression Data: Methods, Challenges, and Opportunities

Preference Learning: An Introduction

Exceptionally Monotone Models -- The Rank Correlation Model Class for Exceptional Model Mining

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