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- W3214166105 abstract "Gene classification and pattern extraction from gene sequence data is essential in understanding different gene sequence features. The field of gene expression data analysis has grown in the past few years from being purely data-centric to integrative, aiming at complementing micro-array analysis with data and knowl-edge from diverse available sources. Since then, it has been used for various science fields including drug discovery, identification of protein coded genes and phenotype prediction based on gene ex-pression. This paper presents an application of gene classification from gene sequence data using data mining and machine learning techniques. Our research's main goal is to compare different ma-chine learning approaches based on time of execution, and over-all efficiency by testing them on different micro-array datasets of gene sequence and determining the best approach for gene clas-sification. Eight different machine learning techniques have been tested on eleven different gene expression datasets. We also ap-ply feature selection method before we apply classification techniques on the gene expression datasets. The experimental results show that feature selection method improve the performance of the techniques on the gene expression datasets. Moreover, we per-form pattern analysis on some gene expression datasets using J 48 decision tree outcome." @default.
- W3214166105 created "2021-11-22" @default.
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- W3214166105 date "2021-12-04" @default.
- W3214166105 modified "2023-10-11" @default.
- W3214166105 title "Gene Expression Data Classification and Pattern Analysis Using Data Driven Approach" @default.
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- W3214166105 doi "https://doi.org/10.1109/icmlc54886.2021.9737248" @default.
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