An investigation of how normalisation and local modelling techniques confound machine learning performance in a mental health study-Reference-Cited by-同舟云学术

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An investigation of how normalisation and local modelling techniques confound machine learning performance in a mental health study

Published:2022-05 Issue:5 Volume:8 Page:e09502
ISSN:2405-8440
Container-title:Heliyon
language:en
Short-container-title:Heliyon

Author:

Zhang Xinxin,Lee Jimmy,Goh Wilson Wen Bin^ORCID

Publisher

Elsevier BV

Subject

Multidisciplinary

Reference48 articles.

1. Batch Effects and Noise in Microarray Experiments: Sources and Solutions;Scherer,2009

2. Application of microarray technology in primate behavioral neuroscience research;Karssen;Methods,2006

3. Tutorial section: there is no silver bullet — a guide to low-level data transforms and normalisation methods for microarray data;Kreil;Briefings Bioinf.,2005

4. Can peripheral blood-derived gene expressions characterize individuals at ultra-high risk for psychosis?;Goh;Comput. Psychiatr. Psychol.,2017

5. GFS: fuzzy preprocessing for effective gene expression analysis;Belorkar;BMC Bioinf.,2016

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1. Comparison of Z-score, min-max, and no normalization methods using support vector machine algorithm to predict student’s timely graduation;AIP Conference Proceedings;2024

2. Perspectives for better batch effect correction in mass-spectrometry-based proteomics;Computational and Structural Biotechnology Journal;2022

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