scholarly journals Fold homology detection using sequence fragment composition profiles of proteins

2010 ◽  
Vol 78 (13) ◽  
pp. 2745-2756 ◽  
Author(s):  
Armando D. Solis ◽  
Shalom R. Rackovsky
Keyword(s):  
Homology Detection ◽  
Fragment Composition ◽  
Sequence Fragment ◽  
Composition Profiles

Cell Surface Glycosylation and Glycosaminoglycan Composition Profiles in Immature and Mature Intervertebral Disks

2012 ◽  
Vol 2 (1_suppl) ◽  
pp. s-0032-1319950-s-0032-1319950
Author(s):  
E. Collin ◽  
M. Kilcoyne ◽  
S. Grad ◽  
M. Alini ◽  
L. Joshi ◽  
...  
Keyword(s):  
Cell Surface ◽  
Intervertebral Disks ◽  
Composition Profiles

scholarly journals FastSK: fast sequence analysis with gapped string kernels

2020 ◽  
Vol 36 (Supplement_2) ◽  
pp. i857-i865
Author(s):  
Derrick Blakely ◽  
Eamon Collins ◽  
Ritambhara Singh ◽  
Andrew Norton ◽  
Jack Lanchantin ◽  
...  
Keyword(s):  
Sequence Analysis ◽  
Dna Sequences ◽  
English Language ◽  
Computation Time ◽  
Entity Recognition ◽  
Supplementary Information ◽  
Support Vector ◽  
Homology Detection ◽  
Scalable Algorithm ◽  
String Kernels

Abstract Motivation Gapped k-mer kernels with support vector machines (gkm-SVMs) have achieved strong predictive performance on regulatory DNA sequences on modestly sized training sets. However, existing gkm-SVM algorithms suffer from slow kernel computation time, as they depend exponentially on the sub-sequence feature length, number of mismatch positions, and the task’s alphabet size. Results In this work, we introduce a fast and scalable algorithm for calculating gapped k-mer string kernels. Our method, named FastSK, uses a simplified kernel formulation that decomposes the kernel calculation into a set of independent counting operations over the possible mismatch positions. This simplified decomposition allows us to devise a fast Monte Carlo approximation that rapidly converges. FastSK can scale to much greater feature lengths, allows us to consider more mismatches, and is performant on a variety of sequence analysis tasks. On multiple DNA transcription factor binding site prediction datasets, FastSK consistently matches or outperforms the state-of-the-art gkmSVM-2.0 algorithms in area under the ROC curve, while achieving average speedups in kernel computation of ∼100× and speedups of ∼800× for large feature lengths. We further show that FastSK outperforms character-level recurrent and convolutional neural networks while achieving low variance. We then extend FastSK to 7 English-language medical named entity recognition datasets and 10 protein remote homology detection datasets. FastSK consistently matches or outperforms these baselines. Availability and implementation Our algorithm is available as a Python package and as C++ source code at https://github.com/QData/FastSK Supplementary information Supplementary data are available at Bioinformatics online.

Composition profiles of (Hg,Cd)Te liquid phase epitaxy layers grown from Te-rich solution

1994 ◽  
Vol 143 (3-4) ◽  
pp. 176-183 ◽  
Author(s):  
V. Jović ◽  
Z. Djurić ◽  
Z. Jakšić ◽  
M. Popović
Keyword(s):  
Liquid Phase ◽  
Liquid Phase Epitaxy ◽  
Rich Solution ◽  
Composition Profiles

Phase composition depth profiles using spatially resolved energy dispersive X-ray diffraction

2004 ◽  
Vol 37 (6) ◽  
pp. 967-976 ◽  
Author(s):  
Andrew C. Jupe ◽  
Stuart R. Stock ◽  
Peter L. Lee ◽  
Nikhila N. Naik ◽  
Kimberly E. Kurtis ◽  
...  
Keyword(s):  
Phase Composition ◽  
Spatial Resolution ◽  
Zinc Coating ◽  
High Energy ◽  
Sulfate Attack ◽  
Energy Dispersive ◽  
X Ray Diffraction ◽  
X Ray ◽  
Spatially Resolved ◽  
Composition Profiles

Spatially resolved energy dispersive X-ray diffraction, using high-energy synchrotron radiation (∼35–80 keV), was used nondestructively to obtain phase composition profiles along the radii of cylindrical cement paste samples to characterize the progress of the chemical changes associated with sulfate attack on the cement. Phase distributions were acquired to depths of ∼4 mm below the specimen surface with sufficient spatial resolution to discern features less than 200 µm thick. The experimental and data analysis methods employed to obtain quantitative composition profiles are described. The spatial resolution that could be achieved is illustrated using data obtained from copper cylinders with a thin zinc coating. The measurements demonstrate that this approach is useful for nondestructively visualizing the sometimes complex transformations that take place during sulfate attack on cement-based materials. These transformations can be spatially related to microstructure as seen by computed microtomography.

The Influence of Gravity on Eutectic PbSn Alloy Grown by the Vertical Bridgman Method

2016 ◽  
Vol 869 ◽  
pp. 637-642
Author(s):  
Rafael Cardoso Toledo ◽  
Chen Y. An ◽  
Irajá Newton Bandeira ◽  
Filipe Estevão de Freitas
Keyword(s):  
Eutectic Alloy ◽  
Thermal Gradient ◽  
Bridgman Method ◽  
Vertical Bridgman Method ◽  
Vertical Bridgman ◽  
Composition Profiles ◽  
Buoyancy Driven Convection ◽  
Gravity Acceleration

Composition profiles of eutectic alloy Pb25.9Sn74.1 atomic % grown by the normal and inverted Bridgman methods are presented and the study of the solute alloy redistribution is made. The inverted vertical Bridgman method, where the solidification occurs from the top to the bottom of the melt under a destabilizing thermal gradient, allows the growth of crystals with buoyancy-driven convection different from that with the usual vertical Bridgman configuration. The scope of this work is to study the influence of the gravity acceleration in the convection process.

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