Occam Razor

Data mining, also referred to as knowledge discovery in databases, is a process of finding new, interesting, previously unknown, potentially useful, and ultimately understandable patterns from very large volumes of data. Data mining is a discipline which brings together database systems, statistics, artificial intelligence, machine learning, parallel and distributed processing and visualization between other disciplinesl.

The objective of DM is to use discovered patterns to help explain current be- havior or to predict future outcome. DM borrows concepts and techniques from several long-established disciplines, among them, Artificial Intelli- gence, Database Technology, Machine Learning and Statistics. The field of DM has, over the past fifteen years, produced a rich variety of algo- rithms that enable computers to learn from large datasets new relation- ships/knowledge.

Occam Razor specialises in designing and implementing complex algorithms for example Artificial Neural Networks (ANN). ANN is the science of creating computational solutions modelled after the brain, like the human brain, neural networks are trainable-once they are taught to solve one complex problem, they can apply their skills to a new set of problems without having to start the learning process from scratch.

Neural networks and machine learning algorithms represent a dramatic departure from conventional programming techniques. Rather than explicitly build a program to solve a problem, neural network "learns" how to solve. For many tasks, neural networks actually outperform human experts. For example, a doctor must go through years of training to learn to diagnose a disease on the basis of a set of symptoms. A neural network, in comparison, would diagnose a disease by first learning from a training set made up of symptoms with the correct diagnoses, and then would formulate a diagnosis when presented with new symptoms on which it was not trained.

"What is Biomedical Informatics?

"Research, development, or application of computational tools and approaches for expanding the use of biological, medical, behavioural or health data, including those to acquire, store, organize, archive, analyze, or visualize such data". (Definition from the National Institutes of Health)

One of the great challenges of biomedical informatics is to discover and/or invent ways to express biomedical data and knowledge in computable form, and to thus be able to automate the analysis and interpretation of a wide range of biomedical data, as well as facilitate access to biomedical data and knowledge

A goal of this Occam-Razor is to refocus thinking about biomedical informatics as a natural, logical, and accessible tool for expressing biomedical knowledge.