Visual data mining using principled projection algorithms and information visualization techniques

Maniyar, Dharmesh M.; Nabney, Ian T.

doi:10.1145/1150402.1150481

Cited by 8 publications

(11 citation statements)

References 18 publications

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“…In addition, the relationships between different libraries (as measured by distance in the LTM space) are better defined, and there is more information about the groupings of compounds within each library. Further interactive exploration of these visualizations can be carried out using the Data Visualization and Modeling System (DVMS) tool (35, 36). …”

Section: Resultsmentioning

confidence: 99%

Increased Diversity of Libraries from Libraries: Chemoinformatic Analysis of Bis‐Diazacyclic Libraries

et al. 2011

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Combinatorial libraries continue to play a key role in drug discovery. To increase structural diversity, several experimental methods have been developed. However, limited efforts have been performed so far to quantify the diversity of the broadly used diversity-oriented synthetic (DOS) libraries. Herein we report a comprehensive characterization of 15 bis-diazacyclic combinatorial libraries obtained through libraries from libraries, which is a DOS approach. Using MACCS keys, radial and different pharmacophoric fingerprints as well as six molecular properties, it was demonstrated the increased structural and property diversity of the libraries from libraries over the individual libraries. Comparison of the libraries to existing drugs, NCI Diversity and the Molecular Libraries Small Molecule Repository revealed the structural uniqueness of the combinatorial libraries (mean similarity < 0.5 for any fingerprint representation). In particular, biscyclic thiourea libraries were the most structurally dissimilar to drugs retaining drug-like character in property space. This study represents the first comprehensive quantification of the diversity of libraries from libraries providing a solid quantitative approach to compare and contrast the diversity of DOS libraries with existing drugs or any other compound collection.

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Section: Resultsmentioning

confidence: 99%

Increased Diversity of Libraries from Libraries: Chemoinformatic Analysis of Bis‐Diazacyclic Libraries

et al. 2011

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“…Recently, we introduced a flexible visual data mining tool which combines advanced projection algorithms developed in the machine learning domain and visual techniques developed in the information visualization domain [28]. Although the rapid development of high-performance computing has to some extent altered our perception of computational complexity, this issue cannot be ignored in a visual data mining framework where user interaction is important.…”

Section: Discussionmentioning

confidence: 99%

Data Visualization with Simultaneous Feature Selection

Maniyar

Nabney

2006

2006 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology

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Abstract-Data visualization algorithms and feature selection techniques are both widely used in bioinformatics but as distinct analytical approaches. Until now there has been no method of deciding feature saliency while training a data visualization model. We derive a generative topographic mapping (GTM) based data visualization approach which estimates feature saliency simultaneously with the training of the visualization model. The approach not only provides a better projection by modeling irrelevant features with a separate noise model but also gives feature saliency values which help the user assess the significance of each feature. We compare the quality of the projection obtained using the new approach with the projections from traditional GTM and self-organizing maps (SOM) algorithms. The results obtained on a synthetic and a real-life chemoinformatics dataset demonstrate that the proposed approach successfully identifies feature significance and provides coherent (compact) projections.

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“…SOFTWARE TOOL A well known framework for information visualization system is Shneiderman's mantra [37] which states: 'Overview first, zoom and filter, then details on demand'. Based on this, a framework was proposed in [38] and a MATLAB-based tool, Data Visualization and Modeling System (DVMS), was developed. It uses principled projection algorithms like PCA, GTM and hierarchical GTM for dimensionality reduction combined with information visualization techniques like scatter plots and parallel coordinates.…”

Section: Gtm-fs Using Log-space Probabilitiesmentioning

confidence: 99%

Novel visualization methods for protein data

Mumtaz

Nabney

Flower

2012

2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)

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Abstract-Visualization of high-dimensional data has always been a challenging task. Here we discuss and propose variants of non-linear data projection methods (Generative Topographic Mapping (GTM) and GTM with simultaneous feature saliency (GTM-FS)) that are adapted to be effective on very highdimensional data. The adaptations use log space values at certain steps of the Expectation Maximization (EM) algorithm and during the visualization process. We have tested the proposed algorithms by visualizing electrostatic potential data for Major Histocompatibility Complex (MHC) class-I proteins. The experiments show that the variation in the original version of GTM and GTM-FS worked successfully with data of more than 2000 dimensions and we compare the results with other linear/nonlinear projection methods: Principal Component Analysis (PCA), Neuroscale (NSC) and Gaussian Process Latent Variable Model (GPLVM).

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Visual data mining using principled projection algorithms and information visualization techniques

Cited by 8 publications

References 18 publications

Increased Diversity of Libraries from Libraries: Chemoinformatic Analysis of Bis‐Diazacyclic Libraries

Increased Diversity of Libraries from Libraries: Chemoinformatic Analysis of Bis‐Diazacyclic Libraries

Data Visualization with Simultaneous Feature Selection

Novel visualization methods for protein data

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