Paper Details

PJB-2025-332

Low-abundance ion-enhanced MS entropy similarity model based on MLP

Jiayao Pan, Ruiyang Li and Yong Li
Abstract

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Traditional similarity methods in small-molecule mass spectrometry are severely hindered by a three-order-of-magnitude signal disparity between high-abundance backbone ions (relative intensity >10%) and low-abundance characteristic ions (relative intensity <1%). To address this limitation, a low-abundance ion–enhanced mass spectrometry entropy (MSE) similarity calculation model based on a multi-layer perceptron (MLP) is proposed. The approach involves four-layer db4 wavelet decomposition, soft-threshold denoising, intensity normalization, and calculation of MSE and statistical features. An MSE-constrained nonlinear function and dual-channel MLP establish spectral peak intensity-dynamic parameter mapping, with backpropagation optimizing parameters to enhance low-abundance ion contribution and suppress high-abundance interference. Validation using the MassBank.us and KUST-MS datasets demonstrate statistically significant performance improvements, with  81.18% (KUST-MS) and 77.27% (MassBank.us) of sample groups achieving t-values greater than 2, and over 50% exhibiting p-values below 0.05.The overall Cohen's d was 0.879, with 88.0% large effect sizes (Cohen's d of 0.8 or higher), and 29.4% extremely large effect (Cohen’s d of 1.5 or higher), confirming dynamic weighting significantly enhances the capability to discriminate structural differences in low-spectral-entropy scenarios

To Cite this article: Pan, J., R. Li and Y. Li. 2026. Low-abundance ion-enhanced MS entropy similarity model based on MLP. Pak. J. Bot., 58(6): DOI: http://dx.doi.org/10.30848/PJB2026-6(5)  
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