Learn More
The ARB (from Latin arbor, tree) project was initiated almost 10 years ago. The ARB program package comprises a variety of directly interacting software tools for sequence database maintenance and analysis which are controlled by a common graphical user interface. Although it was initially designed for ribosomal RNA data, it can be used for any nucleic and(More)
Tubulins are still considered as typical proteins of Eukaryotes. However, more recently they have been found in the unusual bacteria Prosthecobacter (btubAB). In this study, the genomic organization of the btub-genes and their genomic environment were characterized by using the newly developed Two-Step Gene Walking method. In all investigated(More)
MOTIVATION We tackle the problem of finding regularities in microarray data. Various data mining tools, such as clustering, classification, Bayesian networks and association rules, have been applied so far to gain insight into gene-expression data. Association rule mining techniques used so far work on discretizations of the data and cannot account for(More)
We tackle the problem of finding association rules for quantitative data. Whereas most of the previous approaches operate on hyperrectangles, we propose a representation based on half-spaces. Consequently, the left-hand side and right-hand side of an association rule does not contain a conjunction of items or intervals, but a weighted sum of variables(More)
PredictProtein is a meta-service for sequence analysis that has been predicting structural and functional features of proteins since 1992. Queried with a protein sequence it returns: multiple sequence alignments, predicted aspects of structure (secondary structure, solvent accessibility, transmembrane helices (TMSEG) and strands, coiled-coil regions,(More)
We present a new and comprehensive approach to inductive databases in the relational model. The main contribution is a new in-ductive query language extending SQL, with the goal of supporting the whole knowledge discovery process, from pre-processing via data mining to post-processing. A prototype system supporting the query language was developed in the(More)
In the demonstration, we will present the concepts and an implementation of an <i>inductive database</i> -- as proposed by Imielinski and Mannila -- in the relational model. The goal is to support all steps of the knowledge discovery process, from pre-processing via data mining to post-processing, on the basis of queries to a database system. The query(More)
BACKGROUND We present a machine learning approach to the problem of protein ligand interaction prediction. We focus on a set of binding data obtained from 113 different protein kinases and 20 inhibitors. It was attained through ATP site-dependent binding competition assays and constitutes the first available dataset of this kind. We extract information(More)