Anitha Veeramani

Towards ontology-driven navigation of the lipid bibliosphere

BackgroundThe indexing of scientific literature and content is a relevant and contemporary requirement within life science information systems. Navigating information available in legacy formats continues to be a challenge both in enterprise and academic domains. The emergence of semantic web technologies and their fusion with artificial intelligence techniques has provided a new toolkit with which to address these data integration challenges. In the emerging field of lipidomics such navigation challenges are barriers to the translation of scientific results into actionable knowledge, critical to the treatment of diseases such as Alzheimers syndrome, Mycobacterium infections and cancer.ResultsWe present a literature-driven workflow involving document delivery and natural language processing steps generating tagged sentences containing lipid, protein and disease names, which are instantiated to custom designed lipid ontology. We describe the design challenges in capturing lipid nomenclature, the mandate of the ontology and its role as query model in the navigation of the lipid bibliosphere. We illustrate the extent of the description logic-based A-box query capability provided by the instantiated ontology using a graphical query composer to query sentences describing lipid-protein and lipid-disease correlations.ConclusionAs scientists accept the need to readjust the manner in which we search for information and derive knowledge we illustrate a system that can constrain the literature explosion and knowledge navigation problems. Specifically we have focussed on solving this challenge for lipidomics researchers who have to deal with the lack of standardized vocabulary, differing classification schemes, and a wide array of synonyms before being able to derive scientific insights. The use of the OWL-DL variant of the Web Ontology Language (OWL) and description logic reasoning is pivotal in this regard, providing the lipid scientist with advanced query access to the results of text mining algorithms instantiated into the ontology. The visual query paradigm assists in the adoption of this technology.

PREDBALB/c: a system for the prediction of peptide binding to H2d molecules, a haplotype of the BALB/c mouse

PREDBALB/c is a computational system that predicts peptides binding to the major histocompatibility complex-2 (H2d) of the BALB/c mouse, an important laboratory model organism. The predictions include the complete set of H2d class I (H2-Kd, H2-Ld and H2-Dd) and class II (I-Ed and I-Ad) molecules. The prediction system utilizes quantitative matrices, which were rigorously validated using experimentally determined binders and non-binders and also by in vivo studies using viral proteins. The prediction performance of PREDBALB/c is of very high accuracy. To our knowledge, this is the first online server for the prediction of peptides binding to a complete set of major histocompatibility complex molecules in a model organism (H2d haplotype). PREDBALB/c is available at .

Ontology-centric integration and navigation of the dengue literature

Uninhibited access to the unstructured information distributed across the web and in scientific literature databases continues to be beyond the reach of scientists and health professionals. To address this challenge we have developed a literature driven, ontology-centric navigation infrastructure consisting of a content acquisition engine, a domain-specific ontology (in OWL-DL) and an ontology instantiation pipeline delivering sentences derived by domain-specific text mining. A visual query tool for reasoning over A-box instances in the populated ontology is presented and used to build conceptual queries that can be issued to the knowledgebase. We have deployed this generic infrastructure to facilitate data integration and knowledge sharing in the domain of dengue, which is one of the most prevalent viral diseases that continue to infect millions of people in the tropical and subtropical regions annually. Using our unique methodology we illustrate simplified search and discovery on dengue information derived from distributed resources and aggregated according to dengue ontology. Furthermore we apply data mining to the instantiated ontology to elucidate trends in the mentions of dengue serotypes in scientific abstracts since 1974.

CandiVF - Candida albicans Virulence Factor Database

Candida albicans is a pathogen commonly infecting patients who receive immunosuppressive drug therapy, long-term catheterization, or those who suffer from acquired immune deficiency syndrome (AIDS). The major factor accountable for pathogenicity of C. albicans is host immune status. Various virulence molecules, or factors, of are also responsible for the disease progression. Virulence proteins are published in public databases but they normally lack detailed functional annotations. We have developed CandiVF, a specialized database of C. albicans virulence factors (http://antigen.i2r.a-star.edu.sg/Templar/DB/CandiVF/) to facilitate efficient extraction and analysis of data aimed to assist research on immune responses, pathogenesis, prevention, and control of candidiasis. CandiVF contains a large number of annotated virulence proteins, including secretory, cell wall-associated, membrane, cytoplasmic, and nuclear proteins. This database has in-built bioinformatics tools including keyword and BLAST search, visualization of 3D-structures, HLA-DR epitope prediction, virulence descriptors, and virulence factors ontology.

Automatic DAG-Based Service Composition: A Model Checking Approach

We propose a novel approach based on model checking for automated non-linear service composition. Modeling services as interleaved processes, we formulate the service composition problem as verifying a safety property and show that multiple non-linear compositions can be constructed from the counter-example. The state explosion problem is tackled by using service clustering and computing service closures.

Automatic Service Composition via Model Checking

Web service composition is the process of constructing a set of Web services which, when invoked with some user input in a particular order, can produce the output to the users requirements. This paper proposes a novel model checking based approach for automated service composition. Modeling services as a set of interleaved processes in a class of process algebra, we formulate service composition as model checking asserted on a specific type of property on the model. We show that, under this formulation, correct composition workflows can be constructed from the counter-examples provided by model checking. With a case study on online hotel booking services, we demonstrate that the proposed approach can support directed a cyclic composition graphs and the generated composition graphs are automatically verified.

Classification of massive mobile web log URLs for customer profiling & analytics

Many telecommunication companies today have actively started to transform the way they do business, going beyond communication infrastructure providers are repositioning themselves as data-driven service providers to create new revenue streams. In this paper, we present a novel industrial application where a scalable Big data approach combined with deep learning is used successfully to classify massive mobile web log data, to get new aggregated insights on customer web behaviors that could be applied to various industry verticals.

EC2BargainHunter: It's Easy to Hunt for Cost Savings on Amazon EC2!

Return on investment is a critical decision factor for end-users going for cloud deployments. However, major cloud vendors typically provide a myriad of interdependent cloud service options in a variety of purchasing models, that severely complicates cost estimation and optimization. In this paper, we propose a novel Amazon EC2 cost optimization system, called EC2 Bargain Hunter, that innovatively combines services and cloud computing principles with ideas from semantic technologies. The system supports the entire-range of EC2 instance types, and can be used in real-time to perform live cost optimization. We demonstrate that unprecedented cost savings, by a factor of 30, on Amazon EC2 offerings can be found with this system in a few clicks. Furthermore, our approach can be adapted to other IaaS providers, which enables truly real-life cloud cost optimization and thus is a significant step towards making the cloud really cost-effective for the end-users.

Enabling On-Demand Mashups of Open Data with Semantic Services

Analogous to software-as-a-service (SaaS), platform-as-a-service (PaaS) and infrastructure-as-a-service (IaaS), data-as-a-service (DaaS) is used to provide data on demand to users over the Internet and is gaining popularity in the current cloud computing era. In particular, several Open Data initiatives have led to a number of data services in various formats becoming available online, and it remains a challenge to make full use of the data by transferring between and answering queries from different sources in a automatic, dynamic and meaningful manner. In this paper we propose a service-oriented, composition-based approach towards tackling the integration of open data services. We provide a formal, semantic-based modeling of data services and convert the integration problem into the service composition problem. Then the composition graph can be used to create the executable queries to the various data services. We illustrate our idea by using a case study in the real estate domain.