Prediction Based Analysis in Gene Expression Experiments Sample Clauses

Prediction Based Analysis in Gene Expression Experiments. In biology, mechanisms play an important role. As we saw in the genetics Sec- tion (1.2), biology has developed very complex strategies during evolution. As evolution builds upon old strategies [21] there is a lot of redundancy associated with that complexity. This complexity is not easily fully described and makes tak- ing all mechanisms into account a very hard task. It is useful to model the real biology by more general, but complex enough machine learning techniques on a predictive bases. Machine learning techniques learn from seeing data, adjusting the (hyper-)parameters to fit a general view. This general view, usually does not include underlying biological mechanisms. Prediction based analysis is essentially a “black box” based approach. The ma- chine learning designer design the underlying functional relations inside this black box. They then supply intuitional insights into the function of the black box for other researchers (preferably crossing expertise), so that they can apply the algo- rithm to their problem. It is important to point out, that the design of the internals of the black box can have big impacts on results and should not be disregarded completely. However, researchers from other fields of expertise should be able to apply such complex algorithms to their own data without having to fully know the exact underlying code and technique. You should essentially be able to provide the training inputs and output pairs and let the machine learn the patterns in the data (by fitting parameters). After the learning period, we can ask the black box for likely outcomes of newly seen inputs. Most prediction based algorithms try to expose some of the mechanisms (pa- rameters) to the user, so that more insight can be gained into the decision making of the algorithm. This comes in particularly handy when the underlying method assigns importance to newly learned features. This can help to decide which fea- tures the algorithm deems important to solve the prediction from inputs to outputs. In this thesis, we show how to apply software design ideas to implement a Gaussian process in a prediction based way, to be able to directly apply it to gene expression experiments without having to know the exact internals of the underly-