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Bioinformatics and high-throughput sequencing help to identify cancer-specific protein isoforms that serve as potential biomarkers in the early detection of cancer.
Bioinformatics plays an essential role by using its computational tool and methods to collect and store these data. All human genes are simultaneously interrogated by DNA microarray and it has been used for the past many years in the study of cancer.
Apart from analysis of genome sequence data, bioinformatics is now being used for a vast array of other important tasks, including analysis of gene variation and expression, analysis and prediction of gene and protein structure and function, prediction and detection of gene regulation networks, simulation environments
Artificial Intelligence (AI) AI technology is used more and more to help with cancer research and treatment. For example, AI is used to create digital twins for people with cancer, so researchers can predict which treatment will work best for that person.
Applications of data mining to bioinformatics include gene finding, protein function domain detection, function motif detection, protein function inference, disease diagnosis, disease prognosis, disease treatment optimization, protein and gene interaction network reconstruction, data cleansing, and protein sub-cellular
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Introduction of Data Mining Data mining is sometimes called Knowledge Discovery in Database (KDD). It has been successfully applied in bioinformatics, which has abundant data and requires important discoveries such as gene expression, protein modeling, biomarker identification, drug discover and so on.
To accelerate progress, cancer researchers need access to curated data from across many different institutions. Establishing an infrastructure to help researchers store, analyze, integrate, access, and visualize large amounts of biological data and related information is the focus of bioinformatics.

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