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Book ChapterDOI

Biomarker Gene Identification Using a Quantum Inspired Clustering Approach

TL;DR: An unsupervised approach for finding out the significant genes from microarray gene expression datasets using a quantum clustering approach to represent gene-expression data as equations and uses the procedure to search for the most probable set of clusters given the available data.
Abstract: In this paper, we have implemented an unsupervised approach for finding out the significant genes from microarray gene expression datasets. The proposed method is based on implements a quantum clustering approach to represent gene-expression data as equations and uses the procedure to search for the most probable set of clusters given the available data. The main contribution of this approach lies in the ability to take into account the essential features or genes using clustering. Here, we present a novel clustering approach that extends ideas from scale-space clustering and support-vector clustering. This clustering method is used as a feature selection method. Our approach is fundamentally based on the representation of datapoints or features in the Hilbert space, which is then represented by the Schrodinger equation, of which the probability function is a solution. This Schrodinger equation contains a potential function that is extended from the initial probability function.The minima of the potential values are then treated as cluster centres. The cluster centres thus stand out as representative genes. These genes are evaluated using classifiers, and their performance is recorded over various indices of classification. From the experiments, it is found that the classification performance of the reduced set is much better than the entire dataset.The only free-scale parameter, sigma, is then altered to obtain the highest accuracy, and the corresponding biological significance of the genes is noted.
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Journal ArticleDOI
TL;DR: DAMID is a web-accessible program that integrates functional genomic annotations with intuitive graphical summaries that assists in the interpretation of genome-scale datasets by facilitating the transition from data collection to biological meaning.
Abstract: The distributed nature of biological knowledge poses a major challenge to the interpretation of genome-scale datasets, including those derived from microarray and proteomic studies. This report describes DAVID, a web-accessible program that integrates functional genomic annotations with intuitive graphical summaries. Lists of gene or protein identifiers are rapidly annotated and summarized according to shared categorical data for Gene Ontology, protein domain, and biochemical pathway membership. DAVID assists in the interpretation of genome-scale datasets by facilitating the transition from data collection to biological meaning.

8,849 citations

Journal ArticleDOI
TL;DR: DAVID, the database for annotation, visualization and integrated discovery (DAVID), is a web-based online bioinformatics resource that aims to provide tools for the functional interpretation of large lists of genes/proteins.
Abstract: Summary: The database for annotation, visualization and integrated discovery (DAVID), which can be freely accessed at http://david.abcc.ncifcrf.gov/, is a web-based online bioinformatics resource that aims to provide tools for the functional interpretation of large lists of genes/proteins. It has been used by researchers from more than 5000 institutes worldwide, with a daily submission rate of ~1200 gene lists from ~400 unique researchers, and has been cited by more than 6000 scientific publications. However, the current web interface does not support programmatic access to DAVID, and the uniform resource locator (URL)-based application programming interface (API) has a limit on URL size and is stateless in nature as it uses URL request and response messages to communicate with the server, without keeping any state-related details. DAVID-WS (web service) has been developed to automate user tasks by providing stateful web services to access DAVID programmatically without the need for human interactions. Availability: The web service and sample clients (written in Java, Perl, Python and Matlab) are made freely available under the DAVID License at http://david.abcc.ncifcrf.gov/content.jsp?file=WS.html. Contact:xiaoli.jiao@nih.gov; rlempicki@nih.gov

859 citations

Journal ArticleDOI
TL;DR: Various ways of performing dimensionality reduction on high-dimensional microarray data are summarised to provide a clearer idea of when to use each one of them for saving computational time and resources.
Abstract: We summarise various ways of performing dimensionality reduction on high-dimensional microarray data. Many different feature selection and feature extraction methods exist and they are being widely used. All these methods aim to remove redundant and irrelevant features so that classification of new instances will be more accurate. A popular source of data is microarrays, a biological platform for gathering gene expressions. Analysing microarrays can be difficult due to the size of the data they provide. In addition the complicated relations among the different genes make analysis more difficult and removing excess features can improve the quality of the results. We present some of the most popular methods for selecting significant features and provide a comparison between them. Their advantages and disadvantages are outlined in order to provide a clearer idea of when to use each one of them for saving computational time and resources.

749 citations

Journal ArticleDOI
TL;DR: A novel clustering method that is based on physical intuition derived from quantum mechanics, and applicable in higher dimensions by limiting the evaluation of the Schrödinger potential to the locations of data points.
Abstract: We propose a novel clustering method that is based on physical intuition derived from quantum mechanics. Starting with given data points, we construct a scale-space probability function. Viewing the latter as the lowest eigenstate of a Schrodinger equation, we use simple analytic operations to derive a potential function whose minima determine cluster centers. The method has one parameter, determining the scale over which cluster structures are searched. We demonstrate it on data analyzed in two dimensions (chosen from the eigenvectors of the correlation matrix). The method is applicable in higher dimensions by limiting the evaluation of the Schrodinger potential to the locations of data points.

193 citations

Book ChapterDOI
01 Jan 2003

82 citations