6,834 research outputs found
DNA meets the SVD
This paper introduces an important area of computational cell biology where complex, publicly available genomic data is being examined by linear algebra methods, with the aim of revealing biological and medical insights
Semantic distillation: a method for clustering objects by their contextual specificity
Techniques for data-mining, latent semantic analysis, contextual search of
databases, etc. have long ago been developed by computer scientists working on
information retrieval (IR). Experimental scientists, from all disciplines,
having to analyse large collections of raw experimental data (astronomical,
physical, biological, etc.) have developed powerful methods for their
statistical analysis and for clustering, categorising, and classifying objects.
Finally, physicists have developed a theory of quantum measurement, unifying
the logical, algebraic, and probabilistic aspects of queries into a single
formalism. The purpose of this paper is twofold: first to show that when
formulated at an abstract level, problems from IR, from statistical data
analysis, and from physical measurement theories are very similar and hence can
profitably be cross-fertilised, and, secondly, to propose a novel method of
fuzzy hierarchical clustering, termed \textit{semantic distillation} --
strongly inspired from the theory of quantum measurement --, we developed to
analyse raw data coming from various types of experiments on DNA arrays. We
illustrate the method by analysing DNA arrays experiments and clustering the
genes of the array according to their specificity.Comment: Accepted for publication in Studies in Computational Intelligence,
Springer-Verla
Noise and nonlinearities in high-throughput data
High-throughput data analyses are becoming common in biology, communications,
economics and sociology. The vast amounts of data are usually represented in
the form of matrices and can be considered as knowledge networks. Spectra-based
approaches have proved useful in extracting hidden information within such
networks and for estimating missing data, but these methods are based
essentially on linear assumptions. The physical models of matching, when
applicable, often suggest non-linear mechanisms, that may sometimes be
identified as noise. The use of non-linear models in data analysis, however,
may require the introduction of many parameters, which lowers the statistical
weight of the model. According to the quality of data, a simpler linear
analysis may be more convenient than more complex approaches.
In this paper, we show how a simple non-parametric Bayesian model may be used
to explore the role of non-linearities and noise in synthetic and experimental
data sets.Comment: 12 pages, 3 figure
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
Stochastic Data Clustering
In 1961 Herbert Simon and Albert Ando published the theory behind the
long-term behavior of a dynamical system that can be described by a nearly
uncoupled matrix. Over the past fifty years this theory has been used in a
variety of contexts, including queueing theory, brain organization, and
ecology. In all these applications, the structure of the system is known and
the point of interest is the various stages the system passes through on its
way to some long-term equilibrium.
This paper looks at this problem from the other direction. That is, we
develop a technique for using the evolution of the system to tell us about its
initial structure, and we use this technique to develop a new algorithm for
data clustering.Comment: 23 page
A temporal precedence based clustering method for gene expression microarray data
Background: Time-course microarray experiments can produce useful data which can help in understanding the underlying dynamics of the system. Clustering is an important stage in microarray data analysis where the data is grouped together according to certain characteristics. The majority of clustering techniques are based on distance or visual similarity measures which may not be suitable for clustering of temporal microarray data where the sequential nature of time is important. We present a Granger causality based technique to cluster temporal microarray gene expression data, which measures the interdependence between two time-series by statistically testing if one time-series can be used for forecasting the other time-series or not.
Results: A gene-association matrix is constructed by testing temporal relationships between pairs of genes using the Granger causality test. The association matrix is further analyzed using a graph-theoretic technique to detect highly connected components representing interesting biological modules. We test our approach on synthesized datasets and real biological datasets obtained for Arabidopsis thaliana. We show the effectiveness of our approach by analyzing the results using the existing biological literature. We also report interesting structural properties of the association network commonly desired in any biological system.
Conclusions: Our experiments on synthesized and real microarray datasets show that our approach produces encouraging results. The method is simple in implementation and is statistically traceable at each step. The method can produce sets of functionally related genes which can be further used for reverse-engineering of gene circuits
Importance of Similarity Measure in Gene Expression Data-A Survey
The usage of data mining techniques in research fields of computational biology include gene finding, genome assembly , prediction of gene expression etc, are very promising because the large amount of data is involved in these research fields. These techniques aims that to disclose the unknown knowledge and relationships. Different data sources are available one such as DNA Micro Array is the technology which enables the researchers to investigate and address issues which are non traceable. DNA Micro Array experiments generates thousands of gene expression measurements and provide a simple way for collecting huge amounts of data in short time. Micro array data analysis allows identifying the most relevant genes for a target disease and group of genes with similar patterns under different experimental conditions.Clustering methods are widely used on gene expression data to categorize genes with similar expression profiles. The goal of clustering in micro array technology is to group genes or experiments into clusters according to a similarity measure. In this paper we introduce the concept of micro Array technology, clustering on gene expression data and survey on similarity measure. Finally we conclude this paper promising that similarity measure plays an important role on gene expression data while using one of the data mining techniques is clustering
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