Advances in Text and Data Mining of Biological Data: Models, Methods and Applications

The development of biological systems over billions of years has made them very difficult to understand. Biologists and clinical scientists try to understand various biological processes using different tools. However, vast amounts of data for analysis, complex multi-parameter interconnections between the data of a particular set, and hidden relationships between them significantly affect its processing and analysis. The latest advances in Artificial Intelligence (AI), mainly text mining, data mining, artificial neural networks, fuzzy logic, machine learning, and others, can significantly improve the processing of such data. In particular, it creates potential opportunities for doing high-impact investigations that can solve real-world tasks in the system biology branch. The peculiarities of biological data are that it has different types, formats, structures, and huge volumes, which significantly complicates its processing and analysis. Such processing should include models, methods, and tools for efficient storage and retrieval of various types of data; an effective conversion and consolidation of the data of multiple formats; fast optimization and transfer; reliable intellectual analysis to obtain valuable information; as well as informative data visualizations for future visual analysis or better human perception. All this necessitates combining existing and developing new, faster, and precision AI technics for future information discovery and knowledge engineering from such data.