Integrating Bioinformatics and Traditional Chinese Medicine Through Cloud-Based Platforms for Enhanced Healthcare Research
Abstract
The increasing availability of large volumes of biological data and the digitization of classical medical knowledge are rare chances to enhance medical research through integrative analysis. But the synergy between bioinformatics and Traditional Chinese Medicine (TCM) has not proven to be fruitful, thus restricting the potential of bioinformatics in providing holistic interpretation and decision support. In the current paper, a cloud-based platform that combines bioinformatics and TCM is presented and used to exchange and analyze heterogeneous datasets seamlessly. The framework integrates bioinformatics numerical data with symbolic TCM knowledge, which uses preprocessing, semantic mapping, and hybrid analysis layers to assist in offering better decision support. The experimental testing on secondary data, including gene expression data, disease-gene relationship data, and digitized TCM knowledge, shows that all the patterns improve pattern consistency (0.68 to 0.81), interpretability (0.62 to 0.78), analytical stability (0.65 to 0.83), and knowledge alignment accuracy (0.50 to 0.86). This makes for a complementary way of enhancing the interpretability of molecular data using TCM patterns of diagnosis, thus forming an effective model of healthcare research. This cloud computing framework enhances the well-being of humanity as a result of cooperation between bioinformatics and TCM, and it also leads to improved decision support and research in healthcare based on data. The research paper presents some possibilities for leveraging cloud computing technology to make valuable contributions to clinical practices, ontologies, and data-driven healthcare research.