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Detection of Epigenomic Network Community Oncomarkers

arXiv.org Machine Learning

In this paper we propose network methodology to infer prognostic cancer biomarkers based on the epigenetic pattern DNA methylation. Epigenetic processes such as DNA methylation reflect environmental risk factors, and are increasingly recognised for their fundamental role in diseases such as cancer. DNA methylation is a gene-regulatory pattern, and hence provides a means by which to assess genomic regulatory interactions. Network models are a natural way to represent and analyse groups of such interactions. The utility of network models also increases as the quantity of data and number of variables increase, making them increasingly relevant to large-scale genomic studies. We propose methodology to infer prognostic genomic networks from a DNA methylation-based measure of genomic interaction and association. We then show how to identify prognostic biomarkers from such networks, which we term `network community oncomarkers'. We illustrate the power of our proposed methodology in the context of a large publicly available breast cancer dataset.


Facebook will invest tens of millions in community leaders

Engadget

At its Communities Summit in Europe today, Facebook launched its Communities Leadership Program, an initiative meant to "invest in people building communities." The social network has pledged "tens of millions of dollars," including up to $10 million in grants that will fund initiatives proposed by individual awardees. Up to five people will be selected for residencies, while up to 100 will be chosen for a fellowship program. Residents will get up to $1 million for their proposals, while fellows will receive up to $50,000 for specific community initiatives.


Tiles: an online algorithm for community discovery in dynamic social networks

#artificialintelligence

Communities identified by a dynamic community discovery algorithm. Numbers on the edges represent interaction times. A static algorithm, working on the final graph (c), identifies just one community, since it does not take into account the network evolution. In dynamic networks, the rise of new nodes and edges produces deep topological mutations and creates new paths connecting once disconnected components. Therefore, an algorithm that considers social networks as static entities--frozen in time--necessarily introduces bias on its results.


A new method for quantifying network cyclic structure to improve community detection

arXiv.org Machine Learning

A distinguishing property of communities in networks is that cycles are more prevalent within communities than across communities. Thus, the detection of these communities may be aided through the incorporation of measures of the local "richness" of the cyclic structure. In this paper, we introduce renewal non-backtracking random walks (RNBRW) as a way of quantifying this structure. RNBRW gives a weight to each edge equal to the probability that a non-backtracking random walk completes a cycle with that edge. Hence, edges with larger weights may be thought of as more important to the formation of cycles. Of note, since separate random walks can be performed in parallel, RNBRW weights can be estimated very quickly, even for large graphs. We give simulation results showing that pre-weighting edges through RNBRW may substantially improve the performance of common community detection algorithms. Our results suggest that RNBRW is especially efficient for the challenging case of detecting communities in sparse graphs.


Russo

AAAI Conferences

Along with the growth in artifact sharing in online communities such as Flickr, YouTube, and Facebook comes the demand for adding descriptive meta-information, or tags. Tags help individuals to organize and communicate the content and context of their work for themselves and for others. This longitudinal study draws on research in social psychology, network theory and online communities to explain tagging over time. Our findings suggest that tagging increases as a contributor receives attention from others in the community. Further, we find that the more a user's network neighbors are connected to each other directly, the less the focal user will tend to tag his photos. However, density interacts with attention such that those who are surrounded by a dense ego network respond more to attention than others whose ego networks are sparsely interconnected. Unexpectedly, we find no direct correlation between tagging and the individual motivations of enjoyment and commitment. While commitment is not directly associated with tagging, there is an interaction effect such that the effect of commitment on tagging is positive for users with low-density ego networks and negative when a user is surrounded by a high-density network. Directions for future research as well as implications for theory and practice are discussed.