A Machine Learning Approach to Biomarker Screening on Proteomics Data of Cleft Lip and Palate
Authors: 
Weidong Xie, Linjie Wang, Kun Yu, Wei LiAuthors Info & Claims
Weidong Xie, Linjie Wang, Kun Yu, Wei LiAuthors Info & ClaimsPages 59 - 63
Abstract
Genomic and proteomic techniques have provided new research ideas for the early diagnosis of diseases such as cleft lip and palate (CLP). However, a significant challenge is finding the best set of biomarkers for the disease’s clinical diagnosis in massive or high dimensions. Existing studies have focused on refinements and combinations of feature selection methods to improve classification accuracy without interpreting the results. In this paper, seven feature selection methods are firstly compared on eight publicly available genomics microarray data. Moreover, five classification models and two pre-processing methods are aslo compared to find the most appropriate method for processing the microarray data. The method is finally validated on a CLP dataset. The results indicate that the methods described herein can achieve high classification accuracy and provide better feature interpretation.
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Published In
December 2020
239 pages
ISBN:9781450389686
DOI:10.1145/3451421
Copyright © 2020 ACM.
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Association for Computing Machinery
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Published: 27 August 2021
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- Research-article
- Research
- Refereed limited
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- Medical Imaging Processing Engineering Technology Research Center of Shenyang
- The Fundamental Research Funds for the Central Universities
- National Key R&D Program of China
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ISICDM 2020
ISICDM 2020: The Fourth International Symposium on Image Computing and Digital Medicine
December 5 - 7, 2020
Shenyang, China
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