Trust based decision making approach for protein ontology

Biomedical Knowledge of Proteomics Domain is represented in the Protein Ontology, whose instantiations, which are undergoing evolution, need a good management and maintenance system. Protein Ontology instantiations signify information about proteins that is shared and has evolved to reflect development in Protein Ontology Project and Proteomics Domain itself. In this paper we explore the development of a conceptual framework for Protein Ontology instantiations management by using the concepts of trust and reputation in Biomedical Domain. The developed and engineered ontology approach is trustworthy and facilitates reliable additions and updates to the Protein Ontology

WormBase: A modern Model Organism Information Resource

WormBase (https://wormbase.org/) is a mature Model Organism Information Resource supporting researchers using the nematode Caenorhabditis elegans as a model system for studies across a broad range of basic biological processes. Toward this mission, WormBase efforts are arranged in three primary facets: curation, user interface and architecture. In this update, we describe progress in each of these three areas. In particular, we discuss the status of literature curation and recently added data, detail new features of the web interface and options for users wishing to conduct data mining workflows, and discuss our efforts to build a robust and scalable architecture by leveraging commercial cloud offerings. We conclude with a description of WormBase\u27s role as a founding member of the nascent Alliance of Genome Resources

The evaluation of ontologies: Editorial review vs. democratic ranking

Increasingly, the high throughput technologies used by biomedical researchers are bringing about a situation in which large bodies of data are being described using controlled structured vocabularies—also known as ontologies—in order to support the integration and analysis of this data. Annotation of data by means of ontologies is already contributing in significant ways to the cumulation of scientific knowledge and, prospectively, to the applicability of cross-domain algorithmic reasoning in support of scientific advance. This very success, however, has led to a proliferation of ontologies of varying scope and quality. We define one strategy for achieving quality assurance of ontologies—a plan of action already adopted by a large community of collaborating ontologists—which consists in subjecting ontologies to a process of peer review analogous to that which is applied to scientific journal articles

The Cure: Making a game of gene selection for breast cancer survival prediction

Motivation: Molecular signatures for predicting breast cancer prognosis could greatly improve care through personalization of treatment. Computational analyses of genome-wide expression datasets have identified such signatures, but these signatures leave much to be desired in terms of accuracy, reproducibility and biological interpretability. Methods that take advantage of structured prior knowledge (e.g. protein interaction networks) show promise in helping to define better signatures but most knowledge remains unstructured. Crowdsourcing via scientific discovery games is an emerging methodology that has the potential to tap into human intelligence at scales and in modes previously unheard of. Here, we developed and evaluated a game called The Cure on the task of gene selection for breast cancer survival prediction. Our central hypothesis was that knowledge linking expression patterns of specific genes to breast cancer outcomes could be captured from game players. We envisioned capturing knowledge both from the players prior experience and from their ability to interpret text related to candidate genes presented to them in the context of the game. Results: Between its launch in Sept. 2012 and Sept. 2013, The Cure attracted more than 1,000 registered players who collectively played nearly 10,000 games. Gene sets assembled through aggregation of the collected data clearly demonstrated the accumulation of relevant expert knowledge. In terms of predictive accuracy, these gene sets provided comparable performance to gene sets generated using other methods including those used in commercial tests. The Cure is available at http://genegames.org/cure

From genes to behavior: placing cognitive models in the context of biological pathways.

Connecting neural mechanisms of behavior to their underlying molecular and genetic substrates has important scientific and clinical implications. However, despite rapid growth in our knowledge of the functions and computational properties of neural circuitry underlying behavior in a number of important domains, there has been much less progress in extending this understanding to their molecular and genetic substrates, even in an age marked by exploding availability of genomic data. Here we describe recent advances in analytical strategies that aim to overcome two important challenges associated with studying the complex relationship between genes and behavior: (i) reducing distal behavioral phenotypes to a set of molecular, physiological, and neural processes that render them closer to the actions of genetic forces, and (ii) striking a balance between the competing demands of discovery and interpretability when dealing with genomic data containing up to millions of markers. Our proposed approach involves linking, on one hand, models of neural computations and circuits hypothesized to underlie behavior, and on the other hand, the set of the genes carrying out biochemical processes related to the functioning of these neural systems. In particular, we focus on the specific example of value-based decision-making, and discuss how such a combination allows researchers to leverage existing biological knowledge at both neural and genetic levels to advance our understanding of the neurogenetic mechanisms underlying behavior

Ontology based software engineering - software engineering 2.0

This paper describes the use of ontologies in different aspects of software engineering. This use of ontologies varies from support for software developers at multiple sites to the use of an ontology to provide semantics in different categories ofsoftware, particularly on the web. The world's first and only software engineering ontology and a project management ontology in conjunction with a domain ontology are used to provide support for software development that is taking place at multiple sites. Ontologies are used to provide semantics to deal with heterogeneity in the representation of multiple information sources, enable the selection and composition of web services and grid resources, provide the shared knowledge base for multiagent systems, provide semantics and structure for trust and reputation systems and privacy based systems and codification of shared knawledge within different domains in business, science, manufacturing, engineering and utilities. They, therefore, bring a new paradigm to software engineering through the use of semantics as a central mechanism which will revolutionize the way software is developed and consumed in the future leading to the development of software as a service bringing about the dawn of software engineering 2.0