New methods for EXAFS analysis in structural genomics

Bunker, Grant; Dimakis, Nicholas; Khelashvili, Gocha

doi:10.1107/s090904950402881x

Cited by 8 publications

(7 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The code is GA-based for such a multi-objective optimization problem. We should point out we are not the first to apply GA to EXAFS analysis [11]. However a comprehensive study (e.g., crossover and mutation options) of GA algorithms and their effects on uncovering the parameters for materials characterization analysis have not been studied.…”

Section: Design and Implementation Of Ga Analysis Codementioning

confidence: 99%

Automated Materials Spectroscopy Analysis using Genetic Algorithms

Lau¹,

Long²,

Terry³

2022

Preprint

View full text Add to dashboard Cite

We introduce a Genetic Algorithm (GA) based, open-source project to solve multi-objective optimization problems of materials characterization data analysis including EXAFS, XPS and nanoindentation. The modular design and multiple crossover and mutation options make the software extensible for additional materials characterization applications too. This automation of the analysis is crucial in the era when instrumentation acquires data orders of magnitude more rapidly than it can be analyzed by hand. Our results demonstrated good fitness scores with minimal human intervention.

show abstract

Section: Design and Implementation Of Ga Analysis Codementioning

confidence: 99%

Automated Materials Spectroscopy Analysis using Genetic Algorithms

Lau¹,

Long²,

Terry³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…A more detailed introduction to XAS data reduction and data analysis is available in recent reviews. 101,115…”

Section: X-ray Absorption Spectrometry (Xas)mentioning

confidence: 99%

Advanced nuclear analytical techniques for metalloproteomics

Gao

Chen

Chai

2007

J. Anal. At. Spectrom.

View full text Add to dashboard Cite

“…This method, which takes advantage of a priori estimates of model parameters, thus improving the signi®cance of ®ts, has the potential to provide an automated XAS analysis tool (Rehr et al, 2005). Other methods for automation of BioXAS data treatment (Bunker et al, 2005) as for automatic procedures in data quality control have been discussed. The last item is crucial for BioXAS experiments where signals from several (13 to 100) independent¯uorescence detectors are averaged.…”

Section: Metallogenomics and Bioxasmentioning

confidence: 99%