Performance comparison of feature reduction techniques in-terms of compactness, computation time and accuracy

Vijai, Praveen; P, Bagavathi Sivakumar

doi:10.1109/ssci.2018.8628897

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…The information-rich features in metabolomics data are sparse; in fact, they make up only a relatively small portion of the total detected features. Besides, a ML model with too many features takes longer to train, is more prone to overfitting, and suffers from a loss of interpretability; therefore, feature selection strategies which reduce dimensionality are crucial to alleviate the dense data problem that is usually the case in untargeted metabolomics. To start the feature selection procedure in this study, first, we applied a k-Nearest-Neighbors imputation method to find the k nearest samples and imputed the missing elements (Python’s scikit-learn package) .…”

Section: Resultsmentioning

confidence: 99%

Metabolomic and Lipidomic Characterization of Meningioma Grades Using LC–HRMS and Machine Learning

Safari Yazd,

Bazargani,

Fitzpatrick

et al. 2023

J. Am. Soc. Mass Spectrom.

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Meningiomas are among the most common brain tumors that arise from the leptomeningeal cover of the brain and spinal cord and account for around 37% of all central nervous system tumors. According to the World Health Organization, meningiomas are classified into three histological subtypes: benign, atypical, and anaplastic. Sometimes, meningiomas with a histological diagnosis of benign tumors show clinical characteristics and behavior of aggressive tumors. In this study, we examined the metabolomic and lipidomic profiles of meningioma tumors, focusing on comparing low-grade and high-grade tumors and identifying potential markers that can discriminate between benign and malignant tumors. High-resolution mass spectrometry coupled to liquid chromatography was used for untargeted metabolomics and lipidomics analyses of 85 tumor biopsy samples with different meningioma grades. We then applied feature selection and machine learning techniques to find the features with the highest information to aid in the diagnosis of meningioma grades. Three biomarkers were identified to differentiate low-and high-grade meningioma brain tumors. The use of mass-spectrometry-based metabolomics and lipidomics combined with machine learning analyses to prospect and characterize biomarkers associated with meningioma grades may pave the way for elucidating potential therapeutic and prognostic targets.

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Section: Resultsmentioning

confidence: 99%