NEW YORK—Seeking to bring the clinical and molecular data fields together, in silico drug discovery company Life Biosystems and text mining software provider Temis SA have joined forces to create a solution that enables researchers to extract relationships between biomedical entities from large-scale bibliographic information.
The integrated solution, part of a licensing agreement the companies announced in early October, combines Temis' Luxid Annotation Factory with Life Biosystems' in silico discovery infrastructure, creating an accurate, high-performance solution for the analysis of large-scale bibliographic information. The integration will enable Life Biosystems, a company focused on the field of preventive medicine, to quickly produce new clinical insights and hypotheses, says Dr. David B. Jackson, the company's director of in silico discovery.
"We've always strived for accuracy, but now we are going after the speed aspect, because there is always so much happening in the drug development world, and we need to know those things as quickly as possible," Jackson says. "This integration will allow us to monitor the drug discovery environment and regulatory information released by the FDA and incorporate it into our in silico drug discovery work, supporting us in generating novel clinical insights and hypotheses."
Jens Barthelmes, a life sciences consultant at Temis, says he is excited about the synergies created by the integration, and the opportunity to support Life's efforts in the field of predictive medicine.
"It proves that our life science dedicated solution can build upon existing computational infrastructures to provide enhanced analytical and discovery capabilities, by extracting relationships between biomedical entities from text," he says.
Jackson says he believes that Life Biosystems' deployment of Temis' Luxid solution will demonstrate the utility in silico discovery can have on the field of bioinformatics.
"We believe this solution can directly impact the field of bioinformatics, and we intend to provide irrefutable examples of how one can approach issues such as drug resistance using these text mining methods," Jackson says. "This hasn't been achieved to date, and we hope to provide that needed valuation to the medical community." DDN
The integrated solution, part of a licensing agreement the companies announced in early October, combines Temis' Luxid Annotation Factory with Life Biosystems' in silico discovery infrastructure, creating an accurate, high-performance solution for the analysis of large-scale bibliographic information. The integration will enable Life Biosystems, a company focused on the field of preventive medicine, to quickly produce new clinical insights and hypotheses, says Dr. David B. Jackson, the company's director of in silico discovery.
"We've always strived for accuracy, but now we are going after the speed aspect, because there is always so much happening in the drug development world, and we need to know those things as quickly as possible," Jackson says. "This integration will allow us to monitor the drug discovery environment and regulatory information released by the FDA and incorporate it into our in silico drug discovery work, supporting us in generating novel clinical insights and hypotheses."
Jens Barthelmes, a life sciences consultant at Temis, says he is excited about the synergies created by the integration, and the opportunity to support Life's efforts in the field of predictive medicine.
"It proves that our life science dedicated solution can build upon existing computational infrastructures to provide enhanced analytical and discovery capabilities, by extracting relationships between biomedical entities from text," he says.
Jackson says he believes that Life Biosystems' deployment of Temis' Luxid solution will demonstrate the utility in silico discovery can have on the field of bioinformatics.
"We believe this solution can directly impact the field of bioinformatics, and we intend to provide irrefutable examples of how one can approach issues such as drug resistance using these text mining methods," Jackson says. "This hasn't been achieved to date, and we hope to provide that needed valuation to the medical community." DDN
