View source for 20240038514. BAYESIAN DECREMENTAL SCHEME FOR CHARGE STATE DECONVOLUTION simplified abstract (Thermo Finnigan LLC)

=BAYESIAN DECREMENTAL SCHEME FOR CHARGE STATE DECONVOLUTION=


==Organization Name==

[[:Category:Thermo Finnigan LLC|Thermo Finnigan LLC]]


[[Category:Thermo Finnigan LLC]]

==Inventor(s)==

[[:Category:Paul R. Gazis of Mountain View CA (US)|Paul R. Gazis of Mountain View CA (US)]][[Category:Paul R. Gazis of Mountain View CA (US)]]

==BAYESIAN DECREMENTAL SCHEME FOR CHARGE STATE DECONVOLUTION - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240038514 titled 'BAYESIAN DECREMENTAL SCHEME FOR CHARGE STATE DECONVOLUTION

==Simplified Explanation==

The disclosed patent application describes charge state deconvolution systems and methods for analyzing mass spectra. Here is a simplified explanation of the abstract:

* The patent application introduces charge state deconvolution systems and related methods for analyzing mass spectra.
* The charge state deconvolution apparatus includes three main components: first logic to identify peaks in a mass spectrum, second logic to deconvolve the masses of the identified peaks and identify clusters of deconvolved masses with contiguous charge states, and third logic to calculate a Bayesian fitness measure and perform an iterative decremental procedure for charge state deconvolution.
* The first logic identifies peaks in a mass spectrum, which are specific mass-to-charge ratio values representing ions.
* The second logic deconvolves the masses of the identified peaks, meaning it determines the actual mass of each ion based on its mass-to-charge ratio.
* The second logic also identifies clusters of deconvolved masses that have contiguous charge states, meaning they have consecutive charge values.
* The third logic calculates a Bayesian fitness measure, which is a statistical measure of how well the identified clusters fit the expected charge state patterns.
* The third logic then performs an iterative decremental procedure to refine the charge state deconvolution by adjusting the identified clusters based on the fitness measure.

Potential applications of this technology:

* Mass spectrometry analysis: The charge state deconvolution systems and methods can be used in mass spectrometry to accurately determine the charge states of ions, which is crucial for identifying and characterizing molecules in various fields such as proteomics, metabolomics, and drug discovery.
* Protein structure determination: By accurately determining the charge states of ions in mass spectra, this technology can aid in protein structure determination, which is important for understanding protein function and designing therapeutic interventions.

Problems solved by this technology:

* Accurate charge state determination: The technology solves the problem of accurately determining the charge states of ions in mass spectra, which can be challenging due to overlapping peaks and noise in the data.
* Cluster identification: The technology solves the problem of identifying clusters of deconvolved masses with contiguous charge states, which helps in distinguishing true charge state patterns from random noise.

Benefits of this technology:

* Improved data analysis: The charge state deconvolution systems and methods improve the accuracy and reliability of mass spectrometry data analysis by providing a robust approach to determine the charge states of ions.
* Time and cost savings: By automating the charge state deconvolution process, this technology can save time and reduce the need for manual intervention, leading to increased efficiency and cost savings in mass spectrometry analysis.


==Original Abstract Submitted==

disclosed herein are charge state deconvolution systems, as well as related methods, computing devices, and computer-readable media. for example, in some embodiments, a charge state deconvolution apparatus includes first logic to identify peaks in a mass spectrum; second logic to deconvolve the masses of the identified peaks and identify clusters of deconvolved mases that have contiguous charge states; and third logic to calculate a bayesian fitness measure and perform an iterative decremental procedure to perform charge state deconvolution.



[[Category:H01J49/00]]
[[Category:G01N30/86]]
[[Category:G06F18/2415]]