We perform teaching and research in machine learning strategies for the pattern analysis of various kinds of data. This comprises statistical models for clustering, graphical models for network inference and algorithmic methods to efficiently find these structures in the data.
We perform teaching and research in machine learning strategies for the pattern analysis of various kinds of data. This comprises statistical models for clustering, graphical models for network inference and algorithmic methods to efficiently find these structures in the data.
Journal Article - page 5
Learning Dictionaries With Bounded Self-Coherence
Decoding the perception of pain from fMRI using multivariate pattern analysis
Bayesian Mixed-Effects Inference on Classification Performance in Hierarchical Data Sets
Bayesian Mixed-Effects Inference on Classification Performance in Hierarchical Data Sets-2012
A high-throughput metabolomics method to predict high concentration cytotoxicity of drugs from low concentration profiles
A Seven-Marker Signature and Clinical Outcome in Malignant Melanoma: A Large-Scale Tissue-Microarray Study with Two Independent Patient Cohorts
Background Current staging methods such as tumor thickness, ulceration and invasion of the sentinel node are known to be prognostic parameters in patients with malignant melanoma (MM). However, predictive molecular marker profiles for risk stratification and therapy optimization are not yet available for routine clinical assessment. Methods and Findings Using tissue microarrays, we retrospectively analyzed samples from 364 patients with primary MM. We investigated a panel of 70 immunohistochemical (IHC) antibodies for cell cycle, apoptosis, DNA mismatch repair, differentiation, proliferation,...