Cloud computing: a new business paradigm for biomedical information sharing.

We examine how the biomedical informatics (BMI) community, especially consortia that share data and applications, can take advantage of a new resource called "cloud computing". Clouds generally offer resources on demand. In most clouds, charges are pay per use, based on large farms of inexpensive, dedicated servers, sometimes supporting parallel computing. Substantial economies of scale potentially yield costs much lower than dedicated laboratory systems or even institutional data centers. Overall, even with conservative assumptions, for applications that are not I/O intensive and do not demand a fully mature environment, the numbers suggested that clouds can sometimes provide major improvements, and should be seriously considered for BMI. Methodologically, it was very advantageous to formulate analyses in terms of component technologies; focusing on these specifics enabled us to bypass the cacophony of alternative definitions (e.g., exactly what does a cloud include) and to analyze alternatives that employ some of the component technologies (e.g., an institution's data center). Relative analyses were another great simplifier. Rather than listing the absolute strengths and weaknesses of cloud-based systems (e.g., for security or data preservation), we focus on the changes from a particular starting point, e.g., individual lab systems. We often find a rough parity (in principle), but one needs to examine individual acquisitions--is a loosely managed lab moving to a well managed cloud, or a tightly managed hospital data center moving to a poorly safeguarded cloud?

Temporal and Geographic Patterns of Social Media Posts About an Emerging Suicide Game.

PURPOSE: Rates of suicide are increasing rapidly among youth. Social media messages and online games promoting suicide are a concern for parents and clinicians. We examined the timing and location of social media posts about one alleged youth suicide game to better understand the degree to which social media data can provide earlier public health awareness. METHODS: We conducted a search of all public social media posts and news articles on the Blue Whale Challenge (BWC), an alleged suicide game, from January 1, 2013, through June 30, 2017. Data were retrieved through multiple keyword search; sources included social media platforms Twitter, YouTube, Reddit, Tumblr, as well as blogs, forums, and news articles. Posts were classified into three categories: individual "pro"-BWC posts (support for game), individual "anti"-BWC posts (opposition to game), and media reports. Timing and location of posts were assessed. RESULTS: Overall, 95,555 social media posts and articles about the BWC were collected. In total, over one-quarter (28.3%) were "pro"-BWC. The first U.S. news article related to the BWC was published approximately 4 months after the first English language U.S. social media post about the BWC and 9 months after the first U.S. social media post in any language. By the close of the study period, "pro"-BWC posts had spread to 127 countries. CONCLUSIONS: Novel online risks to mental health, such as prosuicide games or messages, can spread rapidly and globally. Better understanding social media and Web data may allow for detection of such threats earlier than is currently possible.

Data integration and genomic medicine.

Genomic medicine aims to revolutionize health care by applying our growing understanding of the molecular basis of disease. Research in this arena is data intensive, which means data sets are large and highly heterogeneous. To create knowledge from data, researchers must integrate these large and diverse data sets. This presents daunting informatic challenges such as representation of data that is suitable for computational inference (knowledge representation), and linking heterogeneous data sets (data integration). Fortunately, many of these challenges can be classified as data integration problems, and technologies exist in the area of data integration that may be applied to these challenges. In this paper, we discuss the opportunities of genomic medicine as well as identify the informatics challenges in this domain. We also review concepts and methodologies in the field of data integration. These data integration concepts and methodologies are then aligned with informatics challenges in genomic medicine and presented as potential solutions. We conclude this paper with challenges still not addressed in genomic medicine and gaps that remain in data integration research to facilitate genomic medicine.

Comparing two approaches for aligning representations of anatomy.

OBJECTIVE: To analyze the comparison, through their results, of two distinct approaches applied to aligning two representations of anatomy. MATERIALS: Both approaches use a combination of lexical and structural techniques. In addition, the first approach takes advantage of domain knowledge, while the second approach treats alignment as a special case of schema matching. The same versions of FMA and GALEN were aligned by each approach. Two thousand one hundred and ninety-nine concept matches were obtained by both approaches. METHODS AND RESULTS: For matches identified by one approach only (337 and 336, respectively), we analyzed the reasons that caused the other approach to fail. CONCLUSIONS: The first approach could be improved by addressing partial lexical matches and identifying matches based solely on structural similarity. The second approach may be improved by taking into account synonyms in FMA and identifying semantic mismatches. However, only 33% of the possible one-to-one matches among anatomical concepts were identified by the two approaches together. New directions need to be explored in order to handle more complex matches.

Challenges in precisely aligning models of human anatomy using generic schema matching.

This paper describes how we used generic schema matching algorithms to align the Foundational Model of Anatomy (FMA) and the GALEN Common Reference Model (CRM), two large models of human anatomy. We summarize the generic schema matching algorithms we used to identify correspondences. We present sample results that highlight the similarities and differences between the FMA and the CRM. We also identify uses of aggregation, transitivity, and reification, for which generic schema matching fails to produce an accurate mapping and present manually constructed solutions for them.

The BioMediator system as a data integration tool to answer diverse biologic queries.

We present the BioMediator (www.biomediator.org) system and the process of executing queries on it. The system was designed as a tool for posing queries across semantically and syntactically heterogeneous data particularly in the biological arena. We use examples from researchers at the University of Washington, and the University of Missouri-Columbia, to discuss the BioMediator system architecture, query execution, modifications to the system to support the queries, and summarize our findings and our future directions. Finally, we discuss the system's flexibility and generalized approach and give examples of how the system can be extended for a variety of objectives.

OQAFMA Querying agent for the Foundational Model of Anatomy: a prototype for providing flexible and efficient access to large semantic networks.

The development of large semantic networks, such as the UMLS, which are intended to support a variety of applications, requires a flexible and efficient query interface for the extraction of information. Using one of the source vocabularies of UMLS as a test bed, we have developed such a prototype query interface. We first identify common classes of queries needed by applications that access these semantic networks. Next, we survey StruQL, an existing query language that we adopted, which supports all of these classes of queries. We then describe the OQAFMA Querying Agent for the Foundational Model of Anatomy (OQAFMA), which provides an efficient implementation of a subset of StruQL by pre-computing a variety of indices. We describe how OQAFMA leverages database optimization by converting StruQL queries to SQL. We evaluate the flexibility and efficiency of our implementation using English queries written by anatomists. This evaluation verifies that OQAFMA provides flexible, efficient access to one such large semantic network, the Foundational Model of Anatomy, and suggests that OQAFMA could be an efficient query interface to other large biomedical knowledge bases, such as the Unified Medical Language System.