http://dl.gi.de/handle/20.500.12116/21225 2026-07-23T01:13:02Z http://dl.gi.de/handle/20.500.12116/20314 Integration and visualisation of multimodal biological data Rohn, Hendrik; Klukas, Christian; Schreiber, Falk Grosse, Ivo; Neumann, Steffen; Posch, Stefan; Schreiber, Falk; Stadler, Peter Understanding complex biological systems requires data from manifold biological levels. Often this data is analysed in some meaningful context, for example, by integrating it into biological networks. However, spatial data given as 2D images or 3D volumes is commonly not taken into consideration and analysed separately. Here we present a new approach to integrate and analyse complex multimodal biological data in space and time. We present a data structure to manage this kind of data and discuss application examples for different data integration scenarios. 2009-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/20313 Converting DNA to music: COMPOSALIGN Ingalls, Todd; Martius, Georg; Hellmuth, Marc; Marz, Manja; Prohaska, Sonja J. Grosse, Ivo; Neumann, Steffen; Posch, Stefan; Schreiber, Falk; Stadler, Peter Alignments are part of the most important data type in the field of comparative genomics. They can be abstracted to a character matrix derived from aligned sequences. A variety of biological questions forces the researcher to inspect these alignments. Our tool, called COMPOSALIGN, was developed to sonify large scale genomic data. The resulting musical composition is based on COMMON MUSIC and allows the mapping of genes to motifs and species to instruments. It enables the researcher to listen to the musical representation of the genome-wide alignment and contrasts a bioinformatician's sight-oriented work at the computer. 2009-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/20312 Identification of cancer and cell-cycle genes with protein interactions and literature mining Royer, Loic; Plake, Conrad; Schroeder, Michael Grosse, Ivo; Neumann, Steffen; Posch, Stefan; Schreiber, Falk; Stadler, Peter Gene prioritization based on background knowledge mined from literature has become an important method for the analysis of results from high-throughput experimental assays such as gene expression microarrays, RNAi screens and genomewide association studies. We apply our gene mention identifier, which achieved the best result of over 80% in the BioCreative II text-mining challenge [HPR+08], and show how text-mined associations can be complemented using guilt-by-association on high confidence protein interaction networks. First, we predict hand-curated gene-disease relationships in the OMIM database, Entrez Gene summaries and GeneRIFs with 37% success rate. Second, we confirm 24% of novel cell-cycle genes identified in a recent RNAi screen [KPH+07] by using text-mining and high confidence protein interactions. Moreover, we show how 71% of GOA cell-cycle annotations can be automatically recovered. Third, we devise a method to rank genes based on novelty, increasing interest, impact, and popularity. 2009-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/20311 CUDA-based multi-core implementation of MDS-based bioinformatics algorithms Fester, Thilo; Schreiber, Falk; Strickert, Marc Grosse, Ivo; Neumann, Steffen; Posch, Stefan; Schreiber, Falk; Stadler, Peter Solving problems in bioinformatics often needs extensive computational power. Current trends in processor architecture, especially massive multi-core processors for graphic cards, combine a large number of cores into a single chip to improve the overall performance. The Compute Unified Device Architecture (CUDA) provides programming interfaces to make full use of the computing power of graphics processing units. We present a way to use CUDA for substantial performance improvement of methods based on multi-dimensional scaling (MDS). The suitability of the CUDA architecture as a high-performance computing platform is studied by adapting a MDS algorithm on specific hardware properties. We show how typical bioinformatics problems related to dimension reduction and network layout benefit from the multi-core implementation of the MDS algorithm. CUDA-based methods are introduced and compared to standard solutions, demonstrating 50-fold acceleration and above. 2009-01-01T00:00:00Z