Knowledge Assembly

Challenges for IT Strategy in Life Sciences

You have put the basic architectures are in place. Target, lead and candidate data can be accessed worldwide, portals have been built to locate, present and publish information, and reporting tools help your colleagues to inspect data.

You know more is needed. What is next in the cycle of knowledge innovation? Semantx proposes this vision of opportunity.

• Look for opportunity in the integration of medical knowledge, not just data, and by mediating across medical specialties.
• Support the creative process of research with semantic systems that understand and extend medical knowledge as it evolves
• Eliminate research directions that have already been tried.
• Provide retrieval of knowledge that reflects whole ideas and new relationships, not only boolean combinations of words and data.
  

Constraining these improvements are powerful forces, which accumulate to increase daily the urgency of change:

• The scale of data growth and new knowledge outpaces the database integration we have already achieved
• Scientists are overloaded by the amount and diversity of available knowledge to review
• Life Science terminology is changing and growing every day
• Today's integration of systems is achieved through manual maintenance of meta data, so that growth of knowledge will produce exponential growth of IT integration costs.
• Manual classifications of knowledge are rapidly outdated. Automated approaches to classification often apply statistical techniques that create accidental links, and have no relevance on the underlying science.

The result: drug discovery cycle times extend, and become many times more complex to manage. Improvement in current approaches to search and knowledge retrieval has reached a dead end. Clearly a new approach to the assembly and filtering of medical knowledge is needed.

Progress demands that we assemble knowledge, and manipulate it based on medical ontologies that reflect the context of each field of research.

Knowledge Assembly will:

• Extract and index knowledge as whole ideas, and do it fast.
• Understand ideas in the semantics of the life science specialty and its neighboring fields of research.
• Bridge among various life science fields to join concepts that are partially linked by common compounds and research vectors
• Locate knowledge using natural language, with feedback to test if the question has been fully understood.
• Identify partial matches of concepts as well as complete and perfect matches.
• Adapt and change knowledge indexing to reflect change and discovery in the science.
  

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Knowledge Assembly in Drug Discovery

For many, the key to improving drug discovery is to manage the whole value chain of research. Information needs to flow seamlessly from fundamental analysis of genomes, proteins and chemistry through to managing clinical trials and drug submissions. But integrating so many fields of life sciences presents a massive task of integrating knowledge and information. There are simply too many disparate ways of recording and using information. Clearly what is needed are bridges which help researchers in one area to communicate with others.

Researchers are staggering under the pressure of new information. New technologies are needed to cope with the large scale of medical data and its range of meaning.

Semantx attacks these problems of knowledge and scale directly. We provide automated analysis of life science language by using ontologies to define ideas and identify relevant data. We deploy this product as a distributed system architecture for speed and scalability.

Semantx has the defining solution for building
Knowledge Bridges and Repositories in drug discovery

Examples

Two examples will help you to understand the rich diversity of applications:

Example 1

On its enterprise portal, a large pharmaceutical company provides standard abstracts for thousands of researchers. But these costly services are anything but standard when used together. Keywords differ across each service, resulting in only partial use of these resources. Worse, these keywords lag behind the state-of-the-science.

Semantx SKIP Solution: the available indexes, abstracts and full text are re-indexed by Semantx to provide a knowledge-based natural language interface. Further, the knowledge, which defines the indexing system, can be updated to reflect proprietary knowledge. So the results mined by employees are better than any other published system in the company's own area of excellence.

Example 2

A company provides specialized services for protein research. To engage the visitors to their web site, they want to allow these customer prospects to search annotations to the genome, and published by the National Institutes of Health.
Semantx SKIP Solution: this database is re-indexed semantically, rendering it searchable by natural language queries . Thus, readers find the right matches, and the company can link them directly to relevant research products.

Let our Technical Liaison specialists help you create specialized applications to serve your needs.

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