A Multi-Level Gene-Disease Based Feature Extraction And Classification Framework For Large Biomedical Document Sets
Keywords:Biomedical documents, document classification, gene-disease rules.
In the current biomedical repositories, gene and disease pattern discovery play a vital role for biomedical document analysis and ranking. Since, most of the biomedical databases have heterogeneous features with different levels of gene and disease patterns. Gene identification and ranking of high dimensional patterns in cross biomedical repositories are complex and difficult to process due to noise, uncertain and missing values. In the traditional biomedical repositories, data classification algorithms are used to classify the documents using the MeSH terms or user specific keywords. Also, these algorithms use static methods to find the relationship among the gene-sets. Therefore, these models are difficult to find the relational genes and its disease patterns in different biomedical repositories. In the proposed work, a hybrid cross gene baseddisease document classification model is proposed using the machine learning framework. In this work, an optimized Glove feature extraction method and advanced classification model are proposed to find key feature sets from the biomedical documents. Experimental results proved that the feature extraction based gene-disease prediction framework has better optimization than the state-of-arttechniques onvarious biomedical disease documents.
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