http://dl.gi.de/handle/20.500.12116/24199 2026-07-21T13:36:05Z 2026-07-21T13:36:05Z Lottaz, Claudio Toedling, Joern Spang, Rainer http://dl.gi.de/handle/20.500.12116/24218 2019-08-12T13:06:24Z 2006-01-01T00:00:00Z

Annotation-based distance measures for patient subgroup discovery in clinical microarray studies Lottaz, Claudio; Toedling, Joern; Spang, Rainer Huson, Daniel; Kohlbacher, Oliver; Lupas, Andrei; Nieselt, Kay; Zell, Andreas Background: Clustering algorithms are widely used in the analysis of microarray data. In clinical studies, they are often applied to find groups of co-regulated genes. Clustering, however, can also stratify patients by similarity of their gene expression profiles, thereby defining novel disease entities based on molecular characteristics. Several distance-based cluster algorithms have been suggested, but little attention has been given to the choice of the distance measure between patients. Even with the Euclidean metric, including and excluding genes from the analysis leads to different distances between the same objects, and consequently different clustering results. Methodology: We describe a novel clustering algorithm, in which gene selection is used to derive biologically meaningful clusterings of samples. Our method combines expression data and functional annotation data. According to gene annotations, candidate gene sets with specific functional characterizations are generated. Each set defines a different distance measure between patients, and consequently different clusterings. These clusterings are filtered using a novel resampling based significance measure. Significant clusterings are reported together with the underlying gene sets and their functional definition. Conclusions: Our method reports clusterings defined by biologically focused sets of genes. In annotation driven clusterings, we have recovered clinically relevant patient subgroups through biologically plausible sets of genes, as well as novel subgroupings. We conjecture that our method has the potential to reveal so far unknown, clinically relevant classes of patients in an unsupervised manner.

2006-01-01T00:00:00Z Küffner, Robert Duchrow, Timo Fundel, Kartin Zimmer, Ralf http://dl.gi.de/handle/20.500.12116/24216 2019-08-12T13:06:23Z 2006-01-01T00:00:00Z

Characterization of protein interactions Küffner, Robert; Duchrow, Timo; Fundel, Kartin; Zimmer, Ralf Huson, Daniel; Kohlbacher, Oliver; Lupas, Andrei; Nieselt, Kay; Zell, Andreas Available information on molecular interactions between proteins is currently incomplete with regard to detail and comprehensiveness. Although a number of repositories are already devoted to capture interaction data, only a small subset of the currently known interactions can be obtained that way. Besides further experiments, knowledge on interactions can only be complemented by applying text extraction methods to the literature. Currently, information to further characterize individual interactions can not be provided by interaction extraction approaches and is virtually nonexistent in repositories. We present an approach to not only confirm extracted interactions but also to characterize interactions with regard to four attributes such as activation vs. inhibition and protein-protein vs. protein-gene interactions. Here, training corpora with positional annotation of interacting proteins are required. As suitable corpora are rare, we propose an extensible curation protocol to conveniently characterize interactions by manual annotation of sentences so that machine learning approaches can be applied subsequently. We derived a training set by manually reading and annotating 269 sentences for 1090 candidate interactions; 439 of these are valid interactions, predicted via support vector machines at a precision of 83% and a recall of 87%. The prediction of interaction attributes from individual sentences on average yielded a precision of about 85% and a recall of 73%.

2006-01-01T00:00:00Z Lise, Stefano Jones, David http://dl.gi.de/handle/20.500.12116/24217 2019-08-12T13:06:23Z 2006-01-01T00:00:00Z

Docking protein domains using a contact map representation Lise, Stefano; Jones, David Huson, Daniel; Kohlbacher, Oliver; Lupas, Andrei; Nieselt, Kay; Zell, Andreas

2006-01-01T00:00:00Z Heinonen, Markus Rantanen, Ari Mielikäinen, Taneli Pitkänen, Esa Kokkonen, Juha Rousu, Juho http://dl.gi.de/handle/20.500.12116/24214 2019-08-12T13:06:20Z 2006-01-01T00:00:00Z

Ab initio prediction of molecular fragments from tandem mass spectrometry data Heinonen, Markus; Rantanen, Ari; Mielikäinen, Taneli; Pitkänen, Esa; Kokkonen, Juha; Rousu, Juho Huson, Daniel; Kohlbacher, Oliver; Lupas, Andrei; Nieselt, Kay; Zell, Andreas Mass spectrometry is one of the key enabling measurement technologies for systems biology, due to its ability to quantify molecules in small concentrations. Tandem mass spectrometers tackle the main shortcoming of mass spectrometry, the fact that molecules with an equal mass-to-charge ratio are not separated. In tandem mass spectrometer molecules can be fragmented and the intensities of these fragments measured as well. However, this creates a need for methods for identifying the generated fragments. In this paper, we introduce a novel combinatorial approach for predicting the structure of molecular fragments that first enumerates all possible fragment candidates and then ranks them according the cost of cleaving a fragment from a molecule. Unlike many existing methods, our method does not rely on hand-coded fragmentation rule databases. Our method is able to predict the correct fragmentation of small-to-medium sized molecules with high accuracy.

2006-01-01T00:00:00Z