Biology subjects

Sarwar, D. M.

Publications and source records attributed to Sarwar, D. M..

4 recordsLinked to original sources

bioRxiv2019

NLIMED: Natural Language Interface for Model Entity Discovery in Biosimulation Model Repositories

Semantic annotation is a crucial step to assure reusability and reproducibility of biosimulation models in biology and physiology. For this purpose, the COmputational Modeling in BIology NEtwork (COMBINE) community recommends the use of the Resource Description Framework (RDF). This grounding in RDF provides the flexibility to enable searching for entities within models (e.g. variables, equations, or entire models) by utilising the RDF query language SPARQL. However, the rigidity and complexity of the SPARQL syntax and the nature of the tree-like structure of semantic annotations, are challenging for users. Therefore, we propose NLIMED, an interface that converts natural language queries into SPARQL. We use this interface to query and discover model entities from repositories of biosimulation models. NLIMED works with the Physiome Model Repository (PMR) and the BioModels database and potentially other repositories annotated using RDF. Natural language queries are first chunked into phrases and annotated against ontology classes and predicates utilising different natural language processing tools. Then, the ontology classes and predicates are composed as SPARQL and finally ranked using our SPARQL Composer and our indexing system. We demonstrate that NLIMEDs approach for chunking and annotating queries is more effective than the NCBO Annotator for identifying relevant ontology classes in natural language queries. Comparison of NLIMEDs behaviour against historical query records in the PMR shows that it can adapt appropriately to queries associated with well-annotated models.

bioinformatics↗

bioRxiv2019

Epithelial Modelling Platform: A Tool for Model Discovery and Assembly with the Physiome Model Repository

In this paper we present a web-based platform enabling scientists to construct a novel epithelial transport model to investigate their hypotheses, aided by building on existing models discovered in the Physiome Model Repository (PMR). We have comprehensively annotated a cohort of epithelial transport models deposited in the PMR as a seeding collection of building blocks that are freely available for reuse. On the platform, users are able to semantically display models for visualization, graphical editing, and model assembly. In addition, we leverage web services from the European Bioinformatics Institute (EBI) to help rank similar models based on the suggestions provided by the platform. In addition to potential use in biomedical and clinical research, novice modellers could use our platform as a learning tool. The source code and links to a live demonstration of the platform are available at https://github.com/dewancse/ epithelial-modelling-platform.

bioinformatics↗

bioRxiv2019

Identifying Brain Region Connectivity using Steiner Minimal Tree Approximation and a Genetic Algorithm

BackgroundComputation and visualization of connectivity among the brain regions is vital for tasks such as disease identification and drug discovery. An effective visualization can aid clinicians and biologists to perform these tasks addressing a genuine research and industrial need. In this paper, we present SMT-Neurophysiology, a web-based tool in a form of an approximation to the Steiner Minimal Tree (SMT) algorithm to search neurophysiology partonomy and connectivity graph in order to find biomedically-meaningful paths that could explain, to neurologists and neuroscientists, the mechanistic relationship, for example, among specific neurophysiological examinations. We also present SMT-Genetic, a web-based tool in a form of a Genetic Algorithm (GA) to find better paths than SMT-Neurophysiology.\n\nResultsWe introduce an approximation to the SMT algorithm to identify the most parsimonious connectivity among the brain regions of interest. We have implemented our algorithm as a highly interactive web application called SMT-Neurophysiology that enables such computation and visualization. It operates on brain region connectivity dataset curated from the Neuroscience Information Framework (NIF) for four species - human, monkey, rat and bird. We present two case studies on finding the most biomedically-meaningful solutions that identifies connections among a set of brain regions over a specific route. The case studies demonstrate that SMT-Neurophysiology is able to find connection among brain regions of interest. Furthermore, SMT-Neurophysiology is modular and generic in nature allowing the underlying connectivity graph to model any data on which the tool can operate. In order to find better connections among a set of brain regions than SMT-Neurophysiology, we have implemented a web-based tool in a form of a GA called SMT-Genetic. We present further three case studies where SMT-Genetic finds better connections among a set of brain regions than SMT-Neurophysiology. SMT-Genetic gives better connections because SMT-Genetic finds global optimum whereas SMT-Neurophysiology finds local optimum although execution time of SMT-Genetic is higher than SMT-Neurophysiology.\n\nConclusionOur analysis would provide key insights to clinical investigators about potential mechanisms underlying a particular neurological disease. The web-based tools and the underlying data are useful to clinicians and scientists to understand neurological disease mechanisms; discover pharmacological or surgical targets; and design diagnostic or therapeutic clinical trials. The source codes and links to the live tools are available at https://github.com/dewancse/connected-brain-regions and https://github.com/dewancse/SMT-Genetic.

bioinformatics↗

bioRxiv2018

Model Annotation and Discovery with the Physiome Model Repository

MotivationMathematics and physics based computational models have the potential to help interpret and encapsulate biological phenomena in a computable and reproducible form. Similarly, comprehensive descriptions of such models encoded in computable form help to ensure that such models are accessible, discoverable, and reusable. To this end, researchers have developed tools and standards to encode mathematical models of biological systems enabling reproducibility and reuse; tools and guidelines to facilitate semantic description of mathematical models; and repositories in which to archive, share, and discover models. Biologists and clinicians can leverage these resources to investigate specific questions and hypotheses.\n\nResultsWe have comprehensively annotated a cohort of models with biological semantics. These annotated models are freely available in the Physiome Model Repository (PMR). To demonstrate the benefits of this approach, we have developed a web-based tool which enables users to discover models relevant to their work, with a particular focus on epithelial transport. In helping a user to discover relevant models this tool will provide users with suggestions for similar or alternative models they may wish to explore or utilize in their model based on the models they would like to use. The semantic annotation and the web tool we have developed is a new contribution enabling scientists to discover relevant models in the PMR as candidates for reuse in their own scientific endeavours. We believe that this approach demonstrates how semantic web technologies and methodologies can contribute to biomedical and clinical research.\n\nAvailability and implementationhttps://github.com/dewancse/model-discovery-tool

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