Approaches to neuroscience data integration

Why Study the History of Neuroscience?

The history of neuroscience is the memory of the discipline and this memory depends on the study of the present traces of the past; the things left behind: artifacts, equipment, written documents, data books, photographs, memoirs, etc. History, in all of its definitions, is an integral part of neuroscience and I have used examples from the literature and my personal experience to illustrate the importance of the different aspects of history in neuroscience. Each time we talk about the brain, do an experiment, or write a research article, we are involved in history. Each published experiment becomes a historical document; it relies on past research (the "Introduction" section), procedures developed in the past ("Methods" section) and as soon as new data are published, they become history and become embedded into the history of the discipline ("Discussion" section). In order to be transparent and able to be replicated, each experiment requires its own historical archive. Studying history means researching books, documents and objects in libraries, archives, and museums. It means looking at data books, letters and memos, talking to scientists, and reading biographies and autobiographies. History can be made relevant by integrating historical documents into classes and by using historical websites. Finally, conducting historical research can be interesting, entertaining, and can lead to travel to out-of-the-way and exotic places and meeting interesting people.

NeuroLex.org: an online framework for neuroscience knowledge

The ability to transmit, organize, and query information digitally has brought with it the challenge of how to best use this power to facilitate scientific inquiry. Today, few information systems are able to provide detailed answers to complex questions about neuroscience that account for multiple spatial scales, and which cross the boundaries of diverse parts of the nervous system such as molecules, cellular parts, cells, circuits, systems and tissues. As a result, investigators still primarily seek answers to their questions in an increasingly densely populated collection of articles in the literature, each of which must be digested individually. If it were easier to search a knowledge base that was structured to answer neuroscience questions, such a system would enable questions to be answered in seconds that would otherwise require hours of literature review. In this article, we describe NeuroLex.org, a wiki-based website and knowledge management system. Its goal is to bring neurobiological knowledge into a framework that allows neuroscientists to review the concepts of neuroscience, with an emphasis on multiscale descriptions of the parts of nervous systems, aggregate their understanding with that of other scientists, link them to data sources and descriptions of important concepts in neuroscience, and expose parts that are still controversial or missing. To date, the site is tracking ~25,000 unique neuroanatomical parts and concepts in neurobiology spanning experimental techniques, behavioral paradigms, anatomical nomenclature, genes, proteins and molecules. Here we show how the structuring of information about these anatomical parts in the nervous system can be reused to answer multiple neuroscience questions, such as displaying all known GABAergic neurons aggregated in NeuroLex or displaying all brain regions that are known within NeuroLex to send axons into the cerebellar cortex.

Brede tools and federating online neuroinformatics databases

As open science neuroinformatics databases the Brede Database and Brede Wiki seek to make distribution and federation of their content as easy and transparent as possible. The databases rely on simple formats and allow other online tools to reuse their content. This paper describes the possible interconnections on different levels between the Brede tools and other databases.

Go green! Reusing brain monitoring data containing missing values: a feasibility study with traumatic brain injury patients

BACKGROUND

Despite the wealth of information carried, periodic brain monitoring data are often incomplete with a significant amount of missing values. Incomplete monitoring data are usually discarded to ensure purity of data. However, this approach leads to the loss of statistical power, potentially biased study and a great waste of resources. Thus, we propose to reuse incomplete brain monitoring data by imputing the missing values - a green solution! To support our proposal, we have conducted a feasibility study to investigate the reusability of incomplete brain monitoring data based on the estimated imputation error.

MATERIALS AND METHODS

Seventy-seven patients, who underwent invasive monitoring of ICP, MAP, PbtO (2) and brain temperature (BTemp) for more than 24 consecutive hours and were connected to a bedside computerized system, were selected for the study. In the feasibility study, the imputation error is experimentally assessed with simulated missing values and 17 state-of-the-art predictive methods. A framework is developed for neuroclinicians and neurosurgeons to determine the best re-usage strategy and predictive methods based on our feasibility study.

RESULTS/CONCLUSION

The monitoring data of MAP and BTemp are more reliable for reuse than ICP and PbtO (2); and, for ICP and PbtO (2) data, a more cautious re-usage strategy should be employed. We also observe that, for the scenarios tested, the lazy learning method, K-STAR, and the tree-based method, M5P, are consistently 2 of the best among the 17 predictive methods investigated in this study.

Semantic Web meets Integrative Biology: a survey

Integrative Biology (IB) uses experimental or computational quantitative technologies to characterize biological systems at the molecular, cellular, tissue and population levels. IB typically involves the integration of the data, knowledge and capabilities across disciplinary boundaries in order to solve complex problems. We identify a series of bioinformatics problems posed by interdisciplinary integration: (i) data integration that interconnects structured data across related biomedical domains; (ii) ontology integration that brings jargons, terminologies and taxonomies from various disciplines into a unified network of ontologies; (iii) knowledge integration that integrates disparate knowledge elements from multiple sources; (iv) service integration that build applications out of services provided by different vendors. We argue that IB can benefit significantly from the integration solutions enabled by Semantic Web (SW) technologies. The SW enables scientists to share content beyond the boundaries of applications and websites, resulting into a web of data that is meaningful and understandable to any computers. In this review, we provide insight into how SW technologies can be used to build open, standardized and interoperable solutions for interdisciplinary integration on a global basis. We present a rich set of case studies in system biology, integrative neuroscience, bio-pharmaceutics and translational medicine, to highlight the technical features and benefits of SW applications in IB.

Semantic SenseLab: Implementing the vision of the Semantic Web in neuroscience

OBJECTIVE

Integrative neuroscience research needs a scalable informatics framework that enables semantic integration of diverse types of neuroscience data. This paper describes the use of the Web Ontology Language (OWL) and other Semantic Web technologies for the representation and integration of molecular-level data provided by several of SenseLab suite of neuroscience databases.

METHODS

Based on the original database structure, we semi-automatically translated the databases into OWL ontologies with manual addition of semantic enrichment. The SenseLab ontologies are extensively linked to other biomedical Semantic Web resources, including the Subcellular Anatomy Ontology, Brain Architecture Management System, the Gene Ontology, BIRNLex and UniProt. The SenseLab ontologies have also been mapped to the Basic Formal Ontology and Relation Ontology, which helps ease interoperability with many other existing and future biomedical ontologies for the Semantic Web. In addition, approaches to representing contradictory research statements are described. The SenseLab ontologies are designed for use on the Semantic Web that enables their integration into a growing collection of biomedical information resources.

CONCLUSION

We demonstrate that our approach can yield significant potential benefits and that the Semantic Web is rapidly becoming mature enough to realize its anticipated promises. The ontologies are available online at http://neuroweb.med.yale.edu/senselab/.