Brendan Elliott

Annotation suggestion and search for personal multimedia objects on the web

The number of personal multimedia objects, such as digital photographs and videos, are exploding on the web through popular sites such as Flickr, YouTube, and FaceBook hosting billions of user-created items. Semantic annotation can be an extremely effective way to search, browse, and organize media objects, but can require extensive human involvement. In this work, we show how semantic metadata about social networks and family relationships can be used to improve semantic annotation suggestion. This includes up to 82% recall for people annotations as well as recall improvements of 20-26% in tag annotation recall when no annotation history is available. In addition, utilizing relationships among people while searching can provide at least 28% higher recall and 55% higher precision than keyword search while still being up to 12 times faster. Methods are evaluated on real personal photo collections containing up to 120k photos from Flickr as well as 41k annotated photos from our prototype system.

PathCase: pathways database system

MOTIVATION As the blueprints of cellular actions, biological pathways characterize the roles of genomic entities in various cellular mechanisms, and as such, their availability, manipulation and queriability over the web is important to facilitate ongoing biological research. RESULTS In this article, we present the new features of PathCase, a system to store, query, visualize and analyze metabolic pathways at different levels of genetic, molecular, biochemical and organismal detail. The new features include: (i) a web-based system with a new architecture, containing a server-side and a client-side, and promoting scalability, and flexible and easy adaptation of different pathway databases, (ii) an interactive client-side visualization tool for metabolic pathways, with powerful visualization capabilities, and with integrated gene and organism viewers, (iii) two distinct querying capabilities: an advanced querying interface for computer savvy users, and built-in queries for ease of use, that can be issued directly from pathway visualizations and (iv) a pathway functionality analysis tool. PathCase is now available for three different datasets, namely, KEGG pathways data, sample pathways from the literature and BioCyc pathways for humans. AVAILABILITY Available online at http://nashua.case.edu/pathways

Efficiently calculating inbreeding on large pedigrees databases

We consider pedigree data structured in the form of a directed acyclic graph, and use an encoding scheme, called NodeCodes, for expediting the evaluation of queries on pedigree graph structures. Inbreeding is the quantitative measure of the genetic relationship between two individuals. The inbreeding coefficient is related to the probability that both copies of any given gene are received from the same ancestor. In this paper we discuss the evaluation of the inbreeding coefficient of a given individual using NodeCodes and propose a new encoding scheme, Family NodeCodes, which is further optimized for pedigree graphs. We implemented and tested these approaches on both synthetic and real pedigree data in terms of performance and scalability. Experimental results show that the use of NodeCodes provides a good alternative for queries involving the inbreeding coefficient, with significant improvements over the traditional iterative evaluation methods (up to 10.1 times faster), and Family NodeCodes further improves this to 77.1 times faster while using 91% less space than regular NodeCodes.

Efficient Evaluation of Inbreeding Queries on Pedigree Data

We consider pedigree data structured in the form of a directed acyclic graph, and use an encoding scheme, called NodeCodes, for expediting the evaluation of queries on pedigree graph structures. Inbreeding is the quantitative measure of the genetic relationship between two individuals. The inbreeding coefficient is related to the probability that both copies of any given gene are received from the same ancestor. In this paper we discuss the evaluation of the inbreeding coefficient of a given individual using NodeCodes. We implemented and tested our approach with both synthetic and real pedigree data. Experimental results show that the use of NodeCodes provides a good alternative for queries involving the inbreeding coefficient, with significant improvements over the traditional iterative evaluation methods.

EFFICIENT COMPUTATION OF KINSHIP AND IDENTITY COEFFICIENTS ON LARGE PEDIGREES

With the rapidly expanding field of medical genetics and genetic counseling, genealogy information is becoming increasingly abundant. An important computation on pedigree data is the calculation of identity coefficients, which provide a complete description of the degree of relatedness of a pair of individuals. The areas of application of identity coefficients are numerous and diverse, from genetic counseling to disease tracking, and thus, the computation of identity coefficients merits special attention. However, the computation of identity coefficients is not done directly, but rather as the final step after computing a set of generalized kinship coefficients. In this paper, we first propose a novel Path-Counting Formula for calculating generalized kinship coefficients, which is motivated by Wrights path-counting method for computing inbreeding coefficient. We then present an efficient and scalable scheme for calculating generalized kinship coefficients on large pedigrees using NodeCodes, a special encoding scheme for expediting the evaluation of queries on pedigree graph structures. Furthermore, we propose an improved scheme using Family NodeCodes for the computation of generalized kinship coefficients, which is motivated by the significant improvement of using Family NodeCodes for inbreeding coefficient over the use of NodeCodes. We also perform experiments for evaluating the efficiency of our method, and compare it with the performance of the traditional recursive algorithm for three individuals. Experimental results demonstrate that the resulting scheme is more scalable and efficient than the traditional recursive methods for computing generalized kinship coefficients.

A comparison of methods for semantic photo annotation suggestion

In this work we describe a general framework for semi-automated semantic digital photo annotation though the use of suggestions. We compare context-based methods with Latent Semantic Indexing, a linear algebra approach to information retrieval. Through experiments on real data sets containing up to 13,705 semantically annotated photos, we show that a carefully chosen combination of context-based methods can not only be efficient, but also extremely effective as well. Furthermore, we propose a new combination of context-based methods that outperforms previous work by up to 19% higher recall while running up to 21 times faster.

Advanced querying interface for biochemical network databases

Querying biochemical networks in flexible ways over the web is important to facilitate ongoing biological research. In this paper, we present a querying interface for biological networks, more specifically, metabolic networks. The interface allows for the specification of a large class of containment, path, and neighborhood queries with ease from a web browser. The query specification process is user-friendly, employs hierarchically arranged relationships among biological entities, and uses autocomplete features. The interface is provided as part of PathCase, a system to store, query, visualize and analyze metabolic pathways at different levels of detail.

Scalable computation of kinship and identity coefficients on large pedigrees.

With the rapidly expanding field of medical genetics and genetic counseling, genealogy information is becoming increasingly abundant. An important computation on pedigree data is the calculation of identity coefficients, which provide a complete description of the degree of relatedness of a pair of individuals. The areas of application of identity coefficients are numerous and diverse, from genetic counseling to disease tracking, and thus, the computation of identity coefficients merits special attention. However, the computation of identity coefficients is not done directly, but rather as the final step after computing a set of generalized kinship coefficients. In this paper, we first propose a novel Path-Counting Formula for calculating generalized kinship coefficients, which is motivated by Wrights path-counting method for computing the inbreeding coefficient for an individual. We then present an efficient and scalable scheme for calculating generalized kinship coefficients on large pedigrees using NodeCodes, a special encoding scheme for expediting the evaluation of queries on pedigree graph structures. We also perform experiments for evaluating the efficiency of our method, and compare it with the performance of the traditional recursive algorithm for three individuals. Experimental results demonstrate that the resulting scheme is more scalable and efficient than the traditional recursive methods for computing generalized kinship coefficients.