Application of Data Mining on Web Usage Data for Security: WebSecuDMiner

Khan, Muhammad Zia Aftab; Park, Jihyun

doi:10.20944/preprints201909.0040.v1

Cited by 1 publication

(1 citation statement)

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“…This is likely due to the inherent noisiness of the data, which is exacerbated by the susceptibility of Graph Neural Networks to noisy data 46,47 . This noisiness arises from the heavy dependence of flavor characterization on human tasters and the influence of genetic, sensory, and environmental factors on flavor perception 3,16,48,49 . It is challenging to implement a denoising strategy without losing valuable information.…”

Section: Flavorminer Combines the Best ML Models For Prediction Of Di...mentioning

confidence: 99%

FlavorMiner: A Machine Learning Platform for Extracting Molecular Flavor Profiles from Structural Data

Herrera-Rocha,

Fernández-Niño,

Duitama

et al. 2024

Preprint

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Flavor is the main factor driving consumers acceptance of food products. However, tracking the biochemistry of flavor is a formidable challenge due to the complexity of food composition. Current methodologies for linking individual molecules to flavor in foods and beverages are expensive and time-consuming. Predictive models based on machine learning (ML) are emerging as an alternative to speed up this process. Nonetheless, the optimal approach to predict flavor features of molecules remains elusive. In this work we present FlavorMiner, an ML-based multilabel flavor predictor. FlavorMiner seamlessly integrates different combinations of algorithms and mathematical representations, augmented with class balance strategies to address the inherent class of the input dataset. Notably, Random Forest and K-Nearest Neighbors combined with Extended Connectivity Fingerprint and RDKit molecular descriptors consistently outperform other combinations in most cases. Resampling strategies surpass weight balance methods in mitigating bias associated with class imbalance. FlavorMiner exhibits remarkable accuracy, with an average ROC AUC score of 0.88. This algorithm was used to analyze cocoa metabolomics data, unveiling its profound potential to help extract valuable insights from intricate food metabolomics data. FlavorMiner can be used for flavor mining in any food product, drawing from a diverse training dataset that spans over 934 distinct food products.

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