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• W2142203320 abstract "Recently, an elegant iterative algorithm called BRAIN (Baffling Recursive Algorithm for Isotopic distributioN calculations) was presented. The algorithm is based on the classic polynomial method for calculating aggregated isotope distributions, and it introduces algebraic identities using Newton-Girard and Viète's formulae to solve the problem of polynomial expansion. Due to the iterative nature of the BRAIN method, it is a requirement that the calculations start from the lightest isotope variant. As such, the complexity of BRAIN scales quadratically with the mass of the putative molecule, since it depends on the number of aggregated peaks that need to be calculated. In this manuscript, we suggest two improvements of the algorithm to decrease both time and memory complexity in obtaining the aggregated isotope distribution. We also illustrate a concept to represent the element isotope distribution in a generic manner. This representation allows for omitting the root calculation of the element polynomial required in the original BRAIN method. A generic formulation for the roots is of special interest for higher order element polynomials such that root finding algorithms and its inaccuracies can be avoided." @default.
• W2142203320 created "2016-06-24" @default.
• W2142203320 creator A5032394557 @default.
• W2142203320 creator A5053819400 @default.
• W2142203320 date "2014-02-12" @default.
• W2142203320 modified "2023-09-25" @default.
• W2142203320 title "BRAIN 2.0: Time and Memory Complexity Improvements in the Algorithm for Calculating the Isotope Distribution" @default.
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• W2142203320 doi "https://doi.org/10.1007/s13361-013-0796-5" @default.
• W2142203320 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3953541" @default.
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