Lior Kirsch

In several epidmic models, the largest eigenvalue of the adjcancy matrix is a proxy for phase transition. It controls whether an epidemic will spread or will it slowly die out. We propose a method to optimaly reduce weights of the edges in a network until a targeted largest eigenvalue is reached and thus stopping the spread of the epidemic.

Almost all measurements collect in the brain represent an uncontrolled mix of various cell types including neurons, glia and blood cells. We propose here a probabilistic approach to blindly demix brain transcriptome samples, building on variability of cell-type proportions across samples. Using this approach we inferring cell-type specific information from these mixtures is a key challenge.

We found that almost all genes in the adult human brain bear a developmental ‘footprint’ which determines their areal expression-pattern based on the developmental ontology of brain regions, while at the same time their spatial expression pattern changes during life.

We identify genes that are primarily expressed in specific brain layers by learning their patterns of expression in images.

A simple interface that enables anyone to highlight information on any webpage and share it

An easy way to visualize brain data using a web interface . For example, here is the expression of the gene HTR1B in the adult human brain

Lets say that we are in the movies recommandation business and instead of just providing yes/no recommendation we want to provide a 5 values scoring system: [“don’t bother”, “bad but watchable”, “nice”, “very nice”, “Shawshank Redemption” ]. We have this ordering in mind that movies that are “nice” are better than “don’t bother” but are worse than “very nice” and also from “Shawshank Redemption” which has a category all of its own.

AUC - Area under the curve, usually refers to the area under the ROC curves. It is common practice in machine learning and in statistics to plot the ROC curves and from there gain information about the behaviour of the classifier. I would like to talk about AUC but not in the classical context of ROC curves. Another less known interpretation, is that AUC is the probability that a positive sample will receive a score higher than a negative sample.