Bioinformatics is an application domain where information is naturally represented in terms of relations between heterogenous objects. Modern experimentation and data acquisition techniques allow the study of complex interactions in biological systems. This raises interesting challenges because the amount of data is huge,some information can not be observed, and measurements may be noisy.

* Using Random Forests to uncover bivariate interactions in high dimensional small data sets. Jorge M. Arevalillo and Hilario Navarro
* Identification of structurally important amino acids in proteins by graphtheoretic measures. Tammy M.K. Cheng, Yu-En Lu and Pietro Li´o
* Lift-based search for significant dependencies in dense data sets. Wilhelmiina Hamalainen
* Finding Optimal Parameters for Edit-Distance Based Sequence Classification is NP-Hard. Vlado Keselj, Haibin Liu, Norbert Zeh, Christian Blouin and Chris Whidden
* Multi-Class Protein Fold Recognition using Large Margin Logic based Divide and Conquer Learning. Huma Lodhi, Stephen Muggleton and Mike J.E. Sternberg
* Protein Sequence Alignment and Intrinsic Disorder: A Substitution Matrix for an Extended Alphabet. Uros Midic, A. Keith Dunker and Zoran Obradovic
* Handling missing values and censored data in PCA of pharmacological matrices. Jan Ramon and Fabrizio Costa
* Comparing Graph-based Representations of Protein for Mining Purposes. Rabie Saidi, Mondher Maddouri and Engelbert M. Nguifo
* Can we improve on the identification of Transcription Factor Binding Sites? Hugh P. Shanahan