MOTIVATION: Deciphering nucleosome-nucleosome interactions is an important step toward mesoscale description of chromatin organization but computational tools to perform such analyses are not publicly available. RESULTS: We developed iNucs, a user-friendly and efficient Python-based bioinformatics tool to compute and visualize nucleosome-resolved interactions using standard pairs format input generated from pairtools. AVAILABILITYAND IMPLEMENTATION: https://github.com/Karimi-Lab/inucs/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Linking topics to specific experts in technical documents and finding connections between experts are crucial for detecting the evolution of emerging topics and the relationships between their influencers in state-of-the-art research. Current techniques that make such connections are limited to similarity measures. Methods based on weights such as TF-IDF and frequency to identify important topics and self joins between topics and experts are generally utilized to identify connections between experts. However, such approaches are inadequate for identifying emerging keywords and experts since the most useful terms in technical documents tend to be infrequent and concentrated in just a few documents. This makes connecting experts through joins on large dense graphs challenging. In this article, we present DIGDUG, a framework that identifies emerging topics by applying graph operations to technical terms. The framework identifies connections between authors of patents and journal papers by performing joins on connected topics and topics associated with the authors at scale. The problem of scaling the graph operations for topics and experts is solved throughdense graph pruningand graph joins categorized under their own scalable separable dense graph class. Experiments were performed on technical domains to validate the utility of the connections between interests and experts. Comparing our graph join and pruning technique against multiple graph and join methods in MapReduce revealed a significant improvement in performance using our approach.

Social media captures voice of customers at a rapid pace. Consumer perception of a brand is crucial to its success. Current techniques for measuring brand perception using lengthy surveys of handpicked users in person, by mail, phone or online are time consuming and increasingly inadequate. A more effective technique to measure brand perception is to interpret customer voice directly from social media and other open data. In this work we present DERIV, a DistributEd, in-memoRy framework for trackIng consumer Voice based on a brand perception measure using storylines generated from open data. The framework measures perception of a brand in comparison to peer brands with in-memory distributed algorithms utilizing supervised machine learning techniques. Experiments performed with open data and models built with storylines of known peer brands show the technique as highly accurate and effective in capturing brand perception.

In item-based collaborative filtering, a critical intermediate step to personalized recommendations is the definition of an item-similarity metric. Existing algorithms compute the item-similarity using the user-to-item ratings (cosine, Pearson, Jaccard, etc.). When computing the similarity between two items A and B many of these algorithms divide the actual number of co-occurring users by some "difficulty" of co-occurrence. We refine this approach by defining item similarity as the ratio of the actual number of co-occurrences to the number of co-occurrences that would be expected if user choices were random. In the final step of our method to compute personalized recommendations we apply the usage history of a user to the item similarity matrix. The well defined probabilistic meaning of our similarities allows us to further improve this final step. We measured the quality of our algorithm on a large real-world data-set. As part of Comcast's efforts to improve its personalized recommendations of movies and TV shows, several top recommender companies were invited to apply their algorithms to one year of Video-on-Demand usage data. Our algorithm tied for first place. This paper includes a MapReduce pseudo code implementation of our algorithm.

This paper discusses a research project: rule-based Web user profiling platform. In this platform, usage data are encoded as a sequence of events, each of which represents an action performed by a user on a Web service at a given time. An event template is proposed to define event models for different Web services. The platform is rule-based. Rules define profile metrics and determine how to compute profile metrics from usage events. A prototype of the platform was implemented and was applied to generate profiles from page view events. The major contribution of the work is the rule-based approach to user profiling. It is the rules and the event template that provide the flexibility to allow the platform to be configured for different Web services.