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- Ralf Eggeling, André Gohr, Pierre-Yves Bourguignon, Edgar Wingender, Ivo Grosse
- ECML/PKDD
- 2013

We introduce inhomogeneous parsimonious Markov models for modeling statistical patterns in discrete sequences. These models are based on parsimonious context trees, which are a generalization of context trees, and thus generalize variable order Markov models. We follow a Bayesian approach, consisting of structure and parameter learning. Structure learning… (More)

- Ralf Eggeling, Teemu Roos, Petri Myllymäki, Ivo Grosse
- BMC Bioinformatics
- 2015

Statistical modeling of transcription factor binding sites is one of the classical fields in bioinformatics. The position weight matrix (PWM) model, which assumes statistical independence among all nucleotides in a binding site, used to be the standard model for this task for more than three decades but its simple assumptions are increasingly put into… (More)

- Ralf Eggeling, Teemu Roos, Petri Myllymäki, Ivo Grosse
- AISTATS
- 2014

Inhomogeneous parsimonious Markov models have recently been introduced for modeling symbolic sequences, with a main application being DNA sequence analysis. Structure and parameter learning of these models has been proposed using a Bayesian approach, which entails the practically challenging choice of the prior distribution. Cross validation is a possible… (More)

- Ralf Eggeling, Mikko Koivisto, Ivo Grosse
- ICML
- 2015

Context trees (CT) are a widely used tool in machine learning for representing context-specific independences in conditional probability distributions. Parsimonious context trees (PCTs) are a recently proposed generalization of CTs that can enable statistically more efficient learning due to a higher structural flexibility, which is particularly useful for… (More)

- Ralf Eggeling, Mikko Koivisto
- UAI
- 2016

We give a novel algorithm for finding a parsimonious context tree (PCT) that best fits a given data set. PCTs extend traditional context trees by allowing context-specific grouping of the states of a context variable, also enabling skipping the variable. However, they gain statistical efficiency at the cost of computational efficiency, as the search space… (More)

Parsimonious Markov models, a generalization of variable order Markov models, have been recently introduced for modeling biological sequences. Up to now, they have been learned by Bayesian approaches. However, there is not always sufficient prior knowledge available and a fully uninformative prior is difficult to define. In order to avoid cumbersome cross… (More)

Model selection, the task of selecting a statistical model from a certain model class given data, is an important problem in statistical learning. From another perspective model selection can also be viewed as learning a single distribution, where the parameter space includes a discrete structure parameter s which imposes further constraints on the… (More)

Parsimonious Markov models have been recently developed as a generalization of variable order Markov models. Many practical applications involve a setting with latent variables, with a common example being mixture models. Here, we propose a Bayesian model averaging approach for learning mixtures of parsimonious Markov models that is based on Gibbs sampling.… (More)

- Ioana M. Lemnian, Ralf Eggeling, Ivo Grosse
- GCB
- 2013

The transcription of genes is often regulated not only by transcription factors binding at single sites per promoter, but by the interplay of multiple copies of one or more transcription factors binding at multiple sites forming a cis-regulatory module. The computational recognition of cisregulatory modules from ChIP-seq or other high-throughput data is… (More)

- Ralf Eggeling, André Gohr, +4 authors Ivo Grosse
- PloS one
- 2014

The binding affinity of DNA-binding proteins such as transcription factors is mainly determined by the base composition of the corresponding binding site on the DNA strand. Most proteins do not bind only a single sequence, but rather a set of sequences, which may be modeled by a sequence motif. Algorithms for de novo motif discovery differ in their promoter… (More)