Biomedical subjects

Florian Sohler

Publications and source records attributed to Florian Sohler.

5 recordsLinked to original sources

PubMed2005

Identifying active transcription factors and kinases from expression data using pathway queries.

MOTIVATION: Although progress has been made identifying regulatory relationships from expression data in general, only few methods have focused on detecting biological mechanisms like active pathways using a single measurement. This is of particular importance when only few measurements are available, e.g. if special cell types or conditions are under investigation. Here we present a method to test user specified hypotheses (pathway queries) on expression data where prior knowledge is given in the form of networks and functional annotations. Based on this method, we develop a scoring function to identify active transcription factors or kinases, thus making a first step toward explaining the measured expression data. RESULTS: We apply the algorithm to the Rosetta Yeast Compendium dataset, finding that in many cases the results are in concordance with biological knowledge. We were able to confirm that transcription factors and to a lesser degree, kinases identified by our method play an important role in the biological processes affected by the respective knockouts. Furthermore, we show that correlation of inferred activities can provide evidence for a physical interaction or cooperation of transcription factors where correlation of plain expression data fails to do so.

Algorithms↗

PubMed2005

Web servicing the biological office.

UNLABELLED: Biologists routinely use Microsoft Office applications for standard analysis tasks. Despite ubiquitous internet resources, information needed for everyday work is often not directly and seamlessly available. Here we describe a very simple and easily extendable mechanism using Web Services to enrich standard MS Office applications with internet resources. We demonstrate its capabilities by providing a Web-based thesaurus for biological objects, which maps names to database identifiers and vice versa via an appropriate synonym list. The client application ProTag makes these features available in MS Office applications using Smart Tags and Add-Ins. AVAILABILITY: http://services.bio.ifi.lmu.de/prothesaurus/

Computational Biology↗

PubMed2004

New methods for joint analysis of biological networks and expression data.

SUMMARY: Biological networks, such as protein interaction, regulatory or metabolic networks, derived from public databases, biological experiments or text mining can be useful for the analysis of high-throughput experimental data. We present two algorithms embedded in the ToPNet application that show promising performance in analyzing expression data in the context of such networks. First, the Significant Area Search algorithm detects subnetworks consisting of significantly regulated genes. These subnetworks often provide hints on which biological processes are affected in the measured conditions. Second, Pathway Queries allow detection of networks including molecules that are not necessarily significantly regulated, such as transcription factors or signaling proteins. Moreover, using these queries, the user can formulate biological hypotheses and check their validity with respect to experimental data. All resulting networks and pathways can be explored further using the interactive analysis tools provided by ToPNet program.

Algorithms↗

PubMed2004

ToPNet--an application for interactive analysis of expression data and biological networks.

SUMMARY: ToPNet is a new tool for the combined visualization and exploration of gene networks and expression data. ToPNet provides various ways of restricting, manipulating and combining biological networks according to annotation data (e.g. Gene Ontology terms) and presents results to the user via different visualization procedures and hyperlinks to the underlying data sources. To easily identify relevant parts of the network, ToPNet provides a method of detecting significant subnetworks with respect to expression measurements. As ToPNet is a pure JAVA application with additional scripting capabilities, it is well-suited as a test-bed for algorithm development and exploratory biological data analysis alike. AVAILABILITY: ToPNet is freely available for academic institutions at http://www.biosolveit.de/ToPNet/

Computer Graphics↗

PubMed2004

Functional genomics of osteoarthritis: on the way to evaluate disease hypotheses.

Functional genomics is a challenging new way to address complex diseases such as osteoarthritis on a molecular level. This complements previous research and will open up new areas of so far unrecognized molecular networks. In this respect, articular cartilage is a good target for functional genomics as it contains only one cell type to which all expression signals can be attributed to. Despite considerable limitations at present, such as a low sensitivity and insensitivity to alternative splicing, posttranscriptional regulation, and posttranslational modification, cDNA-array technology provides a powerful tool to obtain an overview on gene expression patterns hardly achievable with other techniques. This has been shown to be true for known genes as well as for the identification of new genes of interest. Therefore, gene expression analysis will help to identify single genes depending on the disease and experimental conditions investigated. However, the expression pattern of the plethora of expressed genes will paint a picture (network) of disease context, maybe even more pushing forward our understanding of complex diseases such as osteoarthritis.

Cartilage, Articular↗