19 research outputs found
The Stakes in Bayh-Dole: Public Values Beyond the Pace of Innovation
Evaluation studies of the Bayh-Dole Act are generally concerned with the pace of innovation or the transgressions to the independence of research. While these concerns are important, I propose here to expand the range of public values considered in assessing Bayh-Dole and formulating future reforms. To this end, I first examine the changes in the terms of the Bayh-Dole debate and the drift in its design. Neoliberal ideas have had a definitive influence on U.S. innovation policy for the last thirty years, including legislation to strengthen patent protection. Moreover, the neoliberal policy agenda is articulated and justified in the interest of âcompetitiveness.â Rhetorically, this agenda equates competitiveness with economic growth and this with the public interest. Against that backdrop, I use Public Value Failure criteria to show that values such as political equality, transparency, and fairness in the distribution of the benefits of innovation, are worth considering to counter the âpolicy driftâ of Bayh-Dole
Emerging Pattern Mining To Aid Toxicological Knowledge Discovery
Knowledge-based systems for toxicity
prediction are typically based
on rules, known as structural alerts, that describe relationships
between structural features and different toxic effects. The identification
of structural features associated with toxicological activity can
be a time-consuming process and often requires significant input from
domain experts. Here, we describe an emerging pattern mining method
for the automated identification of activating structural features
in toxicity data sets that is designed to help expedite the process
of alert development. We apply the contrast pattern tree mining algorithm
to generate a set of emerging patterns of structural fragment descriptors.
Using the emerging patterns it is possible to form hierarchical clusters
of compounds that are defined by the presence of common structural
features and represent distinct chemical classes. The method has been
tested on a large public <i>in vitro</i> mutagenicity data
set and a public hERG channel inhibition data set and is shown to
be effective at identifying common toxic features and recognizable
classes of toxicants. We also describe how knowledge developers can
use emerging patterns to improve the specificity and sensitivity of
an existing expert system