Rule-based machine learning techniques consist of learning classifier systems, association rule learning, artificial immune systems, and any other technique that relies on a collection of rules containing contextual knowledge. Although rule-based machine learning is essentially a rule-based system, it is unique from traditional rule-based systems, which are often hand-crafted, and other rule-based decision-makers. It is because rule-based machine learning uses a learning algorithm to identify good rules automatically, rather than requiring a human to manually design and curate a rule set using prior domain expertise.

People often talk about artificial intelligence as if it were all one thing. It's more accurate to think of AI as a collection of approaches to problems. An AI system usually combines several approaches, doing whatever produces the best results. The design of a piece of software needs to decide what problem domain it will cover. In other words, what class of problems will it deal with? The narrower the domain is, the easier the job.

People often talk about artificial intelligence as if it were all one thing. It's more accurate to think of AI as a collection of approaches to problems. An AI system usually combines several approaches, doing whatever produces the best results. The design of a piece of software needs to decide what problem domain it will cover. In other words, what class of problems will it deal with? The narrower the domain is, the easier the job.

The inference of biological networks is a highly relevant and challenging task in systems biology and integrative bioinformatics. Biological networks are graphs in which nodes represent genes or proteins, and a connection between them indicates some kind of biological relationship, e.g. regulatory or functional. The network inference is, in an essence, an attempt to reverse engineer the biological relationships from the high-throughput biological data [1]. Most biological network inference methods focus on the definition of gene regulatory networks, in which edges represent direct regulatory interactions between genes [2–4]. Far less effort has been put into the design of methods to build functional networks in which a connection indicates a functional relationship, e.g.