Clinical studies aren’t what they used to be. In the past, the process was one-off: You conducted a study, gathered a lot of data, analyzed it, wrote a report, and submitted it to the authorities. But, says long-time Linked Data advocate Kerstin Forsberg, an information architect at AstraZeneca, that’s all changed in the last few years.

“A study is not a study on its own,” says Forsberg. Today, the goal is  to do meta-analysis across many studies, so parties ranging from  pharmaceuticals companies to contract research organizations to government authorities all are ‘customers’ of clinical data, so to speak. Data from various studies must be shared among all these parties. “It puts a new context around clinical trial data, that it must be easy to link data together, to link across several different studies,” she says.

The case is there to use modern information standards, like semantic web standards and Linked Data principles, to address this need. It’s why Forsberg is one of the individuals spearheading a volunteer effort to create RDF and OWL representations of the standards published by the Clinical Data Interchange Standards Consortium (CDISC) an international, non-profit organization that develops and supports global data standards for medical research.

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It shouldn’t be surprising that Entagen, which makes the semantically-enabled Big Data analytics and collaboration engine TripleMap, has had its sights set on the life sciences space. CEO Christopher Bouton has his Ph.D in molecular neurobiology and has worked at a number of bio tech firms, as well as been the head of integrative data mining at Pfizer – a company that’s using TripleMap for visualized knowledge maps of associations between domain-specific entities (see our story here).

“We see some really compelling and exciting applications of this type of technology in the life sciences space,” says Bouton. But TripleMap can be applied to any scenario where Big Data dots must be connected so that users can collaborate around the understanding of the associations between entities – health care, legal, retail and finance all come to mind.

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This week a new Big Data startup company launched, Ayasdi, co-founded by Stanford mathematics professor Gunner Carlsson and based on his DARPA-funded research in the area of applied topology, with $10+million in Series A funding led by Khosla Ventures and Floodgate.
The technology, dubbed the Insight Discovery platform, is explained to be the “first machine learning platform that combines computer science and a branch of mathematics known as Topological Data Analysis (TDA) that visualizes the entire dataset.” Hundreds of machine learning algorithms, it says, go to work exploring datasets to in minutes automatically discover insights that can’t be determined through query-based or ad hoc approaches.

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At the recent SemTech conference in NYC, The Semantic Web Blog had an opportunity to ask some leaders in the field about where semantic technology has been, and where it’s going.

David Wood, CTO, 3RoundStones:

The short take: Hiring has been on in a big way at semantic tech players as enterprises are moving in greater numbers to buy semantic software, recognizing their traditional vendors won’t solve their interoperability issues. Sem tech vendors should have a happy 2013 as semantics continues going mainstream.

The full take:

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How to help the bench biologist get value out of the wealth of life sciences Linked Data sets? Startup Metaome Science Informatics proposes to offer some help with its DistilBio semantic search and data integration technology, by streamlining the approach to posing user queries. The Distil in DistilBio stands for Data Integration using Semantic Technologies in the Life Sciences.

Metaome, which was founded by CEO Kalpana Krishnaswami and CTO Ramkumar Nandakumar as a bioinformatics services provider before transitioning to a product vendor, contains a few more than a dozen life sciences public data sets so far. Infomaticians in the life sciences space have the expertise to query such data across sets via SPARQL, but the front-line biologist isn’t necessarily an infomatician. So, DistilBio has created a query interface that makes it easier for them to ask large and complex questions in a simplified way across data sets while building a graph in the process.

“How does a user say what are the drugs used for Alzheimer’s disease and do have they have certain protein targets and are those protein targets implicated in other diseases?” says Krishnaswami. “To ask that in one shot right now is hard without working through a SPARQL endpoint using all the SPARQL syntax.”

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The Semantic Web Journal has issued a call for papers on the topic of linked data for health care and the life sciences. According to the post, “Due to their descriptive nature and ongoing need to integrate large amounts of heterogeneous data, the areas of health care and the life sciences have long been used as a test-bed for the feasibility of Semantic Web technologies. Large scale integration projects like Bio2RDF, Chem2Bio2RDF, and the W3C HCLS’s (Health Care and Life Sciences) Linked Open Drug Data (LODD) have not only significantly contributed to the development of the Semantic Web’s Linked Data effort, but have also made social and technical contributions towards data integration, knowledge management, and knowledge discovery.” Read more

A new paper has been published entitled “Systems Chemical Biology and the Semantic Web: What They Mean for the Future of Drug Discovery Research.” The paper was written by David Wild, Ying Ding, Amit Sheth, Lee Harland, Eric Gifford, and Michael Lajiness. It can be downloaded for a fee of $27.95. According to the abstract, “Systems chemical biology, the integration of chemistry, biology and computation to generate understanding about the way small molecules affect biological systems as a whole, as well as related fields such as chemogenomics, are central to emerging new paradigms of drug discovery such as drug repurposing and personalized medicine.” Read more

Bioinformatics software provider IO Informatics recently released its free Knowledge Explorer Personal Edition. Version 3.6 of the Personal Edition can handle most of what Knowledge Explorer Professional 3.6, launched in October, can, but it does all its work in memory without direct connectivity to a back-end database.

“In particular, a lot of the strengths of Knowledge Explorer have to do with modeling data as RDF and then testing queries, visualizing and browsing the data to see that you have the ontologies and data mappings you need for your integration and application requirements.” says Robert Stanley, IO Informatics president and CEO. The Personal version is aimed at academic experts focused on data integration and semantic data modeling, as well as personal power users in life sciences and other data-intensive industries, or anyone who wants to learn the tool in anticipation of leveraging their enterprise data sets for collaboration and integration projects.

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A few months ago FeirceBiotechIT named five biotech IT firms to watch, and they haven’t been disappointed with what they’ve seen: “This report gives FierceBiotechIT a chance to feature 5 software groups that are addressing a range of different needs for biotechs and other life sciences firms. Here you’ll find a Silicon Valley upstart taking on big competitors with new technology for integrating data from disparate sources and providing developers with insights about their clinical trials. There’s a Boston-area company using advanced analytics to spot biomarkers and other tantalizing details about health with the aid of supercomputing technology. And another group based in the U.K. has been quietly growing with R&D software originally licensed from Merck.” Learn more about the five companies here. Read more

This video lecture discusses the development of OpenTox, an open source predictive toxicology framework. The description states, “A new paradigm of 21st century human-oriented safety testing approaches is now emerging based on a combination of in silico and in vitro approaches. The new predictive test systems developed from this growing ‘grand challenge’ effort will need to combine evidences from a great variety of data, protocols, and concepts. The combination of these sources of knowledge within an ontology-based mechanistic knowledge-oriented framework to produce reliable test systems demands the development of a semantic web for toxicology.” Read more