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ArticlesSection default policy
Integration of Artificial Intelligence with Network Biology
Recent advancements in experimental technologies have expanded the availability and quantity of data in biology. There has been a steady growth in demand for technology that is intelligent and can react and perform in the situation more accurately. Advances in digital technology are redefining the way we deliver technology to people and make them comfortable with the use of tech functions. Given the importance of interactions in biological processes, such as the interactions between proteins or the bonds within a chemical compound, this data is often represented in the form of a biological network. The rise of this data has created a need for new computational tools to analyze networks. One major trend in the field is to use Ai algorithm which more specifically uses methods that work with networks.
Biological databases that once comprised sequences and structures of compounds have now advanced into the storage of more complex and bulk data. Most Microarray Profiling studies are based upon a limited subset of the complete expression dataset. We realize that full potential can only be reached upon integration and unification of all available data. And Ai is playing a vital role in the integration and unification of datasets. The ability of AI to make informed decisions, learn and perceive the environment, and predict certain behavior, among its many other skills, makes this application of paramount importance in today's world. For decades, we have tried building computational models for teaching machines.
However, one major setback here is the amount of variation in data collected from different sources. As we know, we shall be able to achieve optimized results only when we would be able to integrate data from a variety of different sources and then devise an automated learning algorithm to analyze and infer prediction based on previous learning experiences.
Keywords: Artificial Intelligence, Machine Learning; Signal Processing; E-Health, Deep
Learning, Convolutional Neural Network, Biological networks, Bioengineering
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