A Real-time Cancer-Covid Gene-Set Based Biomedical Document Classification and Ranking Framework for Large Databases

International Journal of Electronics and Communication Engineering

© 2023 by SSRG - IJECE Journal

Volume 10 Issue 9

Year of Publication : 2023

Authors : Jose Mary Golamari, D. Haritha

10.14445/23488549/IJECE-V10I9P108

How to Cite?

Jose Mary Golamari, D. Haritha, "A Real-time Cancer-Covid Gene-Set Based Biomedical Document Classification and Ranking Framework for Large Databases," SSRG International Journal of Electronics and Communication Engineering, vol. 10,  no. 9, pp. 72-80, 2023. Crossref, https://doi.org/10.14445/23488549/IJECE-V10I9P108

Abstract:

Identifying and ranking gene and disease patterns are essential for analyzing and ranking biomedical documents in current biomedical repositories. However, the presence of noise, uncertainty, and missing values in most biomedical databases, coupled with their diverse features and varying levels of gene and disease patterns, makes identifying and ranking high-dimensional patterns across different repositories a complex and challenging task. Data classification algorithms rely on MeSH terms or user-specific keywords to classify documents in conventional biomedical repositories. Nevertheless, these algorithms use static methods to establish relationships among gene sets, which may need to be revised for accurate analysis and ranking of biomedical documents. Locating cancer and COVID genes associated with diseases and their patterns in biomedical repositories is a difficult task. A novel Cancer-Covid gene/disease document classification and ranking approach has been suggested, employing a cross-gene model with machine learning techniques. The proposed method employs an optimized Glove feature extraction technique and an advanced classification model to identify significant features from biomedical documents. Experimental results indicate that this feature extraction method is more effective than other existing techniques in predicting gene-disease relationships in various biomedical documents. 

Keywords:

Cross-domain analysis, Cancer genesets, Covid gene sets. 

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