Analysis of protein-protein interaction networks by means of annotated graph mining algorithms

This thesis discusses solutions to several open problems in Protein-Protein Interaction (PPI) networks with the aid of Knowledge Discovery. PPI networks are usually represented as undirected graphs, with nodes corresponding to proteins and edges representing interactions among protein pairs. A large amount of available PPI data and noise within it has made the knowledge discovery process a necessary central part for the network analysis. We define Knowledge Discovery as a process of extracting informative knowledge from the huge amount of data. Much success has been achieved when the input data is represented as a set of independent instances and their attributes. But, in the context of PPI networks, there is interesting knowledge to be mined from the relationships between instances (proteins). The resulting research area is called ``Graph Mining''. Here, the input data is modeled as a graph and the output could be any type of knowledge. In this thesis, we propose several graph mining algorithms to examine structural characteristics of PPI networks and link them to the information useful for biologists, such as function or disease.LEI Universiteit LeidenThis research is supported by the Dutch Science Foundation (NWO) through a VIDI grant.Algorithms and the Foundations of Software technolog

On Cognitive Preferences and the Plausibility of Rule-based Models

It is conventional wisdom in machine learning and data mining that logical models such as rule sets are more interpretable than other models, and that among such rule-based models, simpler models are more interpretable than more complex ones. In this position paper, we question this latter assumption by focusing on one particular aspect of interpretability, namely the plausibility of models. Roughly speaking, we equate the plausibility of a model with the likeliness that a user accepts it as an explanation for a prediction. In particular, we argue that, all other things being equal, longer explanations may be more convincing than shorter ones, and that the predominant bias for shorter models, which is typically necessary for learning powerful discriminative models, may not be suitable when it comes to user acceptance of the learned models. To that end, we first recapitulate evidence for and against this postulate, and then report the results of an evaluation in a crowd-sourcing study based on about 3.000 judgments. The results do not reveal a strong preference for simple rules, whereas we can observe a weak preference for longer rules in some domains. We then relate these results to well-known cognitive biases such as the conjunction fallacy, the representative heuristic, or the recogition heuristic, and investigate their relation to rule length and plausibility.Comment: V4: Another rewrite of section on interpretability to clarify focus on plausibility and relation to interpretability, comprehensibility, and justifiabilit