While the direct goal of biological modeling is to describe data, it ultimately aims to find ways of fixing systems and enhancing understanding of system objectives, algorithms and mechanisms. This is due to machine learning making it possible to model data extremely well, without using strong assumptions about the modeled system. EurekAlert! brought this interesting information to our attention in their article, “Applying machine learning to biomedical science.”
Machine learning can usually describe data better than biomedical models and thus provide both engineering solutions and an essential benchmark. It can also be a tool to advance understanding.
Because more minds make better results, ensemble deep learning combines multiple computers to achieve high levels of performance.
Many kinds of questions can be answered using machine learning techniques. Often the goal is not only to describe and predict data but also to produce models that can be readily understood and taught.
Melody K. Smith
Sponsored by Access Innovations, the world leader in taxonomies, metadata, and semantic enrichment to make your content findable.