The SHOGUN machine learning toolbox's focus is on large scale kernel methods and especially on Support Vector Machines (SVM). It comes with a generic interface for SVMs, features several SVM and [...]

binsdfc is a command line implementation of the algorithm described in [Endres,Oram,Schindelin,Foldiak:Bayesian binning beats approximate alternatives: estimating peri-stimulus time histograms, [...]

Nested Effects Models (NEMs) are a class of directed graphical models originally introduced to analyze the effects of gene perturbation screens with high-dimensional phenotypes. In contrast to other [...]

The Sleipnir C++ library implements a variety of machine learning and data manipulation algorithms focusing on heterogeneous data integration and efficiency for large biological data collections.

The Easysvm package provides a set of tools based on the Shogun toolbox allowing to train and test SVMs in a simple way.

For modern biology, precise genome annotations are of prime importance as they allow the accurate definition of genic regions. We employ state of the art machine learning methods to assay and [...]

Local alignment kernels measure the similarity between two sequences by summing up scores obtained from local alignments with gaps of the sequences.

PALMA computes the optimal spliced alignment of a mRNA sequence to a genomic sequence. The main python script takes two FASTA files containing the target (e.g. a DNA sequence, part of the genome) [...]