Interfacing Between Programming Languages Important

Computer programming languages’ interoperability has seemingly always been an elusive proposition. The concept of providing interfaces between languages is more important than ever, notes David Chisnall of the University of Cambridge. “With software becoming ever more complex and hardware less homogeneous, the likelihood of a single language being the correct tool for an entire program is lower than ever.” Applications using high-level languages typically call code written in lower-level languages, for example. Making such interfaces is challenging, but increasingly important. “The industry has spent the past 30 years building CPUs optimized for running languages such as C, because people who needed fast code used C. … Maybe the time has come to start exploring better built-in support for common operations in other languages.” The paper was published online by ACM Queue. (SlashDot)(ACM Queue)
 

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New method to find novel connections from gene to gene, drug to drug and between scientists

ScienceDaily (July 24, 2012) — Researchers from Mount Sinai School of Medicine have developed a new computational method that will make it easier for scientists to identify and prioritize genes, drug targets, and strategies for repositioning drugs that are already on the market. By mining large datasets more simply and efficiently, researchers will be able to better understand gene-gene, protein-protein, and drug/side-effect interactions. The new algorithm will also help scientists identify fellow researchers with whom they can collaborate.

Led by Avi Ma'ayan, PhD, Assistant Professor of Pharmacology and Systems Therapeutics at Mount Sinai School of Medicine, and Neil Clark, PhD a postdoctoral fellow in the Ma'ayan laboratory, the team of investigators used the new algorithm to create 15 different types of gene-gene networks. They also discovered novel connections between drugs and side effects, and built a collaboration network that connected Mount Sinai investigators based on their past publications.

"The algorithm makes it simple to build networks from data," said Dr. Ma'ayan. "Once high dimensional and complex data is converted to networks, we can understand the data better and discover new and significant relationships, and focus on the important features of the data."

The group analyzed one million medical records of patients to build a network that connects commonly co-prescribed drugs, commonly co-occurring side effects, and the relationships between side effects and combinations of drugs. They found that reported side effects may not be caused by the drugs, but by a separate condition of the patient that may be unrelated to the drugs. They also looked at 53 cancer drugs and connected them to 32 severe side effects. When chemotherapy was combined with cancer drugs that work through cell signaling, there was a strong link to cardiovascular related adverse events. These findings can assist in post-marketing surveillance safety of approved drugs.

The approach is presented in two separate publications in the journals BMC Bioinformatics and BMC Systems Biology. The tools that implement the approach Genes2FANs and Sets2Networks can be found online at http://actin.pharm.mssm.edu/genes2FANs and http://www.maayanlab.net/S2N.

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The above story is reprinted from materials provided by The Mount Sinai Hospital / Mount Sinai School of Medicine, via Newswise.

Note: Materials may be edited for content and length. For further information, please contact the source cited above.

Journal Reference:

Ruth Dannenfelser, Neil R Clark, Avi Ma'ayan. Genes2FANs: connecting genes through functional association networks. BMC Bioinformatics, 2012; 13 (1): 156 DOI: 10.1186/1471-2105-13-156

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Disclaimer: This article is not intended to provide medical advice, diagnosis or treatment. Views expressed here do not necessarily reflect those of ScienceDaily or its staff.

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