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.
Publications - page 7
Prediction of colorectal cancer diagnosis based on circulating plasma proteins
Non‐invasive detection of colorectal cancer with blood‐based markers is a critical clinical need. Here we describe a phased mass spectrometry‐based approach for the discovery, screening, and validation of circulating protein biomarkers with diagnostic value. Initially, we profiled human primary tumor tissue epithelia and characterized about 300 secreted and cell surface candidate glycoproteins. These candidates were then screened in patient systemic circulation to identify detectable candidates in blood plasma. An 88‐plex targeting method was established to systematically monitor these proteins in...
Obtaining Consensus Annotations For Retinal Image Segmentation Using Random Forest And Graph Cuts
Kickback cuts Backprop’s red-tape: Biologically plausible credit assignment in neural networks
Joint Segmentation and Groupwise Registration of Cardiac DCE MRI Using Sparse Data Representations
Inversion of hierarchical Bayesian models using Gaussian processes
Inferring causal metabolic signals that regulate the dynamic TORC1-dependent transcriptome
Cells react to nutritional cues in changing environments via the integrated action of signaling, transcriptional, and metabolic networks. Mechanistic insight into signaling processes is often complicated because ubiquitous feedback loops obscure causal relationships. Consequently, the endogenous inputs of many nutrient signaling pathways remain unknown. Recent advances for system‐wide experimental data generation have facilitated the quantification of signaling systems, but the integration of multi‐level dynamic data remains challenging. Here, we co‐designed dynamic experiments and a probabilistic, model‐based method to infer causal...
Greedy MaxCut algorithms and their information content
Crowdsourcing the creation of image segmentation algorithms for connectomics
To stimulate progress in automating the reconstruction of neural circuits, we organized the first international challenge on 2D segmentation of electron microscopic (EM) images of the brain. Participants submitted boundary maps predicted for a test set of images, and were scored based on their agreement with a consensus of human expert annotations. The winning team had no prior experience with EM images, and employed a convolutional network. This “deep learning” approach has since become accepted as a standard for segmentation...