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 “competitiveness.” 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 “policy drift” 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