Just how difficult can it be counting up R&D funding for emerging technologies (and is tech mining with proxy measures going to be any better?)

Decision makers considering policy or strategy related to the development of emerging technologies expect high quality data on the support for different technological options. A natural starting point would be R&D funding statistics. This paper explores the limitations of such aggregated data in relation to the substance and quantification of funding for emerging technologies. Using biotechnology as an illustrative case, we test the utility of a novel taxonomy to demonstrate the endemic weaknesses in the availability and quality of data from public and private sources. Using the same taxonomy, we consider the extent to which tech-mining presents an alternative, or potentially complementary, way to determine support for emerging technologies using proxy measures such as patents and scientific publications

Data Management Roles for Librarians

In this Chapter:● Looking at data through different lenses● Exploring the range of data use and data support ● Using data as the basis for informed decision making ● Treating data as a legitimate scholarly research produc

Function2Gene: A gene selection tool to increase the power of genetic association studies by utilizing public databases and expert knowledge

<p>Abstract</p> <p>Background</p> <p>Many common disorders have multiple genetic components which convey increased susceptibility. SNPs have been used to identify genetic components which are associated with a disease. Unfortunately, many studies using these methods suffer from low reproducibility due to lack of power.</p> <p>Results</p> <p>We present a set of programs which implement a novel method for searching for disease-associated genes using prior information to select and order genes from publicly available databases by their prior likelihood of association with the disease. These programs were used in a published study of childhood-onset SLE which yielded novel associations with modest sample size.</p> <p>Conclusion</p> <p>Using prior information to decrease the size of the problem space to an amount commensurate with available samples and resources while maintaining appropriate power enables researchers to increase their likelihood of discovering reproducible associations.</p

Integrative genomic mining for enzyme function to enable engineering of a non-natural biosynthetic pathway.

The ability to biosynthetically produce chemicals beyond what is commonly found in Nature requires the discovery of novel enzyme function. Here we utilize two approaches to discover enzymes that enable specific production of longer-chain (C5-C8) alcohols from sugar. The first approach combines bioinformatics and molecular modelling to mine sequence databases, resulting in a diverse panel of enzymes capable of catalysing the targeted reaction. The median catalytic efficiency of the computationally selected enzymes is 75-fold greater than a panel of naively selected homologues. This integrative genomic mining approach establishes a unique avenue for enzyme function discovery in the rapidly expanding sequence databases. The second approach uses computational enzyme design to reprogramme specificity. Both approaches result in enzymes with &gt;100-fold increase in specificity for the targeted reaction. When enzymes from either approach are integrated in vivo, longer-chain alcohol production increases over 10-fold and represents &gt;95% of the total alcohol products