Deployment of open source recommendation systems has been shown to be an effective way to increase sale conversions on a variety of e-commerce sites. However, there remains a large gap between deploying the core algorithm provided by these systems and delivering an application-quality recommendation system, specifically tailored to address complex and dynamically changing business needs. We will present a real-time recommendation engine built on our graph data platform that provides the following extensions to a basic recommendation model: True real-time recommendation algorithms: We provide a simple framework for customers to author and deploy real-time recommendation algorithms with no pre-computation required. Streaming updates of user behavior and product information: As quickly as data are generated, the recommendation engine applies the updates and can serve updated results. Support for offline recommendation algorithms}: Users with existing investment in a quality recommendation program can import their pre-computed results into the graph database for efficient, unified service of results. Tools for Business-centric requirements: The engine offers a range of weighting, sorting, and filtering options to tailor recommendation algorithms to business needs. For example, the engine can eliminate products that are out of stock or favor products that are known to perform well in different real-time contexts. Multiple algorithm ensemble support: There is rarely a case where one algorithm is sufficient to identify the best items to recommend to a user. Integrating points 1 through 4, the engine provides intuitive methods for specifying and combining the results of multiple recommendation algorithms to achieve the highest-performing results. Recommendation feedback tools: Pre- and Post-analysis tools, built around a business' logic, are used to generate reports to assess the value of both potential and currently deployed algorithms.

Community detection has been one of the fundamental problems in network analysis. Results from community detection (for example, grouping of products by latent category) can also serve as information nuggets to other business applications, such as product recommendation or taxonomy building. Because several real networks are naturally directed, e.g. World Wide Web, some recent studies proposed algorithms for detecting various types of communities in a directed network. However, few of them considered that nodes play two different roles, source and terminal, in a directed network.

MOTIVATION: Inferring the underlying regulatory pathways within a gene interaction network is a fundamental problem in Systems Biology to help understand the complex interactions and the regulation and flow of information within a system-of-interest. Given a weighted gene network and a gene in this network, the goal of an inference algorithm is to identify the potential regulatory pathways passing through this gene. RESULTS: In a departure from previous approaches that largely rely on the random walk model, we propose a novel single-source k-shortest paths based algorithm to address this inference problem. An important element of our approach is to explicitly account for and enhance the diversity of paths discovered by our algorithm. The intuition here is that diversity in paths can help enrich different functions and thereby better position one to understand the underlying system-of-interest. Results on the yeast gene network demonstrate the utility of the proposed approach over extant state-of-the-art inference algorithms. Beyond utility, our algorithm achieves a significant speedup over these baselines. AVAILABILITY: All data and codes are freely available upon request.

MOTIVATION: In recent years, Markov clustering (MCL) has emerged as an effective algorithm for clustering biological networks-for instance clustering protein-protein interaction (PPI) networks to identify functional modules. However, a limitation of MCL and its variants (e.g. regularized MCL) is that it only supports hard clustering often leading to an impedance mismatch given that there is often a significant overlap of proteins across functional modules. RESULTS: In this article, we seek to redress this limitation. We propose a soft variation of Regularized MCL (R-MCL) based on the idea of iteratively (re-)executing R-MCL while ensuring that multiple executions do not always converge to the same clustering result thus allowing for highly overlapped clusters. The resulting algorithm, denoted soft regularized Markov clustering, is shown to outperform a range of extant state-of-the-art approaches in terms of accuracy of identifying functional modules on three real PPI networks. AVAILABILITY: All data and codes are freely available upon request. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Advances in high-throughput technology has led to an increased amount of available data on protein-protein interaction (PPI) data. Detecting and extracting functional modules that are common across multiple networks is an important step towards understanding the role of functional modules and how they have evolved across species. A global protein-protein interaction network alignment algorithm attempts to find such functional orthologs across multiple networks. RESULTS: In this article, we propose a scalable global network alignment algorithm based on clustering methods and graph matching techniques in order to detect conserved interactions while simultaneously attempting to maximize the sequence similarity of nodes involved in the alignment. We present an algorithm for multiple alignments, in which several PPI networks are aligned. We empirically evaluated our algorithm on three real biological datasets with 6 different species and found that our approach offers a significant benefit both in terms of quality as well as speed over the current state-of-the-art algorithms. CONCLUSION: Computational experiments on the real datasets demonstrate that our multiple network alignment algorithm is a more efficient and effective algorithm than the state-of-the-art algorithm, IsoRankN. From a qualitative standpoint, our approach also offers a significant advantage over IsoRankN for the multiple network alignment problem.