Benchmarking beat classification algorithms

Nabney, Ian T.; Evans, David; Tenner, J.; Gamlyn, Lee

doi:10.1109/cic.2001.977709

Cited by 5 publications

(6 citation statements)

References 9 publications

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“…Separate neural networks were trained for each combination of detector, time period, and energy bin using the standard back-propagation-of-errors training algorithm [64]. Once the neural networks were trained, they assigned a numerical value to each event in the range 0-1.…”

Section: B Neural-network Timing Analysismentioning

confidence: 99%

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Improved WIMP-search reach of the CDMS II germanium data

Agnese¹,

Anderson²,

Asai³

et al. 2015

Phys. Rev. D

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CDMS II data from the 5-tower runs at the Soudan Underground Laboratory were reprocessed with an improved charge-pulse fitting algorithm. Two new analysis techniques to reject surfaceevent backgrounds were applied to the 612 kg days germanium-detector WIMP-search exposure. An extended analysis was also completed by decreasing the 10 keV analysis threshold to ∼5 keV, to increase sensitivity near a WIMP mass of 8 GeV/c 2 . After unblinding, there were zero candidate events above a deposited energy of 10 keV and 6 events in the lower-threshold analysis. This yielded minimum WIMP-nucleon spin-independent scattering cross-section limits of 1.8×10 −44 and 1.18×10 −41 cm 2 at 90% confidence for 60 and 8.6 GeV/c 2 WIMPs, respectively. This improves the previous CDMS II result by a factor of 2.4 (2.7) for 60 (8.6) GeV/c 2 WIMPs.PACS numbers: 95.35.+d, 95.30.Cq, 85.25.Oj, 29.40.Wk

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Section: B Neural-network Timing Analysismentioning

confidence: 99%

“…The NETLAB package [64] for MATLAB was used to perform this analysis. Training samples for surface events and NRs were selected from the 133 Ba and 252 Cf calibration data, respectively.…”

Section: B Neural-network Timing Analysismentioning

confidence: 99%

Improved WIMP-search reach of the CDMS II germanium data

Agnese¹,

Anderson²,

Asai³

et al. 2015

Phys. Rev. D

View full text Add to dashboard Cite

show abstract

“…The basic implementation of GTM approach has been taken from the Netlab package (MATLAB toolbox for neural networks and pattern recognition, version 3.3). , Additional MATLAB procedures have been written to apply GTM to the classification problem. Principal component analysis (PCA) has been used as a preprocessing step in the model development.…”

Section: Methodsmentioning

confidence: 99%

Toward Navigating Chemical Space of Ionic Liquids: Prediction of Melting Points Using Generative Topographic Maps

Kireeva

Кузнецов

Tsivadze

2012

Ind. Eng. Chem. Res.

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In this work, we apply generative topographic maps as a universal approach for data visualization and structure–property modeling of melting points (mp), which is one of the most important physical properties for the design and application of ionic liquids (ILs) as green solvents. Data visualization is part of a more general concept of chemography, which is a relatively new field dealing with visualization of chemical data, representation of chemical space, and navigation in this space. This field has received much attention by chemists as it may help to analyze and to intuitively comprehend relevant molecular features and relationships. In this study, to our knowledge for the first time, we proposed the universal approach that can be used both for the visualization of the chemical space of ILs according to their melting point values and for the development of the classification models able to predict the melting points of novel ILs. The structurally diverse data set of 717 ILs containing bromides of nitrogen-containing organic cations and including 126 pyridinium bromides (PYR), 384 imidazolium and benzoimidazolium bromides (IMZ), and 207 quaternary ammonium bromides (QUAT) was involved in model development. This study was carried out in several descriptor spaces analyzing the impact of descriptor choice. The clear criteria for data visualization and classification quality were used to assess the performance of the developed models.

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“…We constructed a 2D map corresponding to the descriptors using the GTM proposed by Bishop (see Appendix). Netlab toolbox (a MATLAB toolbox) was used for visualization with GTM.…”

Section: Case Studiesmentioning

confidence: 99%

Chemical-Space-Based de Novo Design Method To Generate Drug-Like Molecules

Takeda

Kaneko

Funatsu

2016

J. Chem. Inf. Model.

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To discover drug compounds in chemical space containing an enormous number of compounds, a structure generator is required to produce virtual drug-like chemical structures. The de novo design algorithm for exploring chemical space (DAECS) visualizes the activity distribution on a two-dimensional plane corresponding to chemical space and generates structures in a target area on a plane selected by the user. In this study, we modify the DAECS to enable the user to select a target area to consider properties other than activity and improve the diversity of the generated structures by visualizing the drug-likeness distribution and the activity distribution, generating structures by substructure-based structural changes, including addition, deletion, and substitution of substructures, as well as the slight structural changes used in the DAECS. Through case studies using ligand data for the human adrenergic alpha2A receptor and the human histamine H1 receptor, the modified DAECS can generate high diversity drug-like structures, and the usefulness of the modification of the DAECS is verified.

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Benchmarking beat classification algorithms

Cited by 5 publications

References 9 publications

Improved WIMP-search reach of the CDMS II germanium data

Improved WIMP-search reach of the CDMS II germanium data

Toward Navigating Chemical Space of Ionic Liquids: Prediction of Melting Points Using Generative Topographic Maps

Chemical-Space-Based de Novo Design Method To Generate Drug-Like Molecules

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