A Review on Biomedical Mining

A wide range of biomedical repositories is available in the distributed systems for clinical decision making. One of the most important biomedical repositories is PubMed, which gives access to more than 50 million documents from MEDLINE. Data mining is used to explore hidden and unknown patterns from the large databases. The unstructured and uncertainty problems are available for many domain fields such as biomedical repositories, biomedical databases, web mining, health care system, education and technology intensive companies due to its large size. Information extraction from biomedical repositories and analyzing this information with an experimental study is time-consuming and requires an efficient feature selection and classification models. The latest trends of text mining are able to answer many different research queries, ranging from the biomarkers, gene discovery, gene-disease prediction and drug discovery from biomedical repositories. As a result, text mining has evolved in the field of biomedical systems where text mining techniques and machine learning models are integrated using high computational resources. The initial contribution of this research is to discuss the background study about biomedical repositories, document classification, clustering the relevant documents based on MeSH terms, methodologies, and models used in Biomedical Document analysis using the Hadoop framework. The main purpose of the work is to explain about the importance of feature extraction and document classification models to find gene-disease patterns from the massive biomedical repositories.

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