Biomedical Data Mining and Transform Framework

Data serves as a valuable resource rich in useful information that individuals seek to leverage, particularly within the biomedical sector. Every hour, laboratories, hospitals, and medical facilities generate substantial amounts of data. Biomedical data encompasses critical information, including disease diagnosis results, treatment regimens, and manifestations indicating abnormal gene relationships among groups of cancer and non-cancer patients, as well as patient samples with cirrhosis and primary liver cancer. The information requirements of biomedical researchers are extensive; they seek insights into survival rates, disease-free survival, and recurrence to develop and validate disease prognosis models. Researchers face significant challenges in interpreting data due to its volume and diverse formats, which include images, audio, and both structured and unstructured text. Consequently, there is a pressing need for data mining software tools to furnish researchers in the biomedical field with essential information. This paper aims to propose a framework equipped with functions to convert unstructured text data generated from biomedical laboratories into a structured format and to apply various statistical methods for data analysis, thereby offering recommendations to assist researchers in swiftly identifying genes with abnormal expressions through data analysis.