We chose high content cell imaging to read out our screening assay, an information rich method that facilitated monitoring of multiple phenotypes.
This article presents a discussion regarding ways in which data-rich methods can inform cognitive engineering.
While recent work on predictability has resulted in richer methods and incorporated interesting new features such as social contacts, they have not identified the intrinsic characteristics of human mobility that form the basis for the limitations in predicting the next visited location.
The asymptotic runtime of data-rich methods has also been neglected.
We selected capillary electrophoresis, density gradient ultracentrifugation (DGU), and mass spectrometry, as the most information-rich methods and amenable to clinical studies.
However, the application of these variable-rich (i.e., end point-rich) methods to multiple stressor exposures has been limited to a narrow-range of genetic backgrounds.
Many data-rich methods have been published with claims of good accuracy, but to the best of our knowledge, efficiency and runtime have not yet been compared with state-of-the-art, 'simulate-and-match' parameter estimation methods.
Estimating ODE parameters has been studied in the mathematical literature for decades, and an important class of data-rich methods called 'multiple shooting' (Bock, 1983; Bock and Schlöder, 1986a, b) has recently been applied to biological networks (Peifer and Timmer, 2007).
A simpler detection assay giving a positive or negative identification of H5N1 will be helpful, but a more information-rich method is necessary to provide additional information to guide patient care or public health measures, especially as new strains emerge.
A few people became very rich, adopting methods reminiscent of, but even more ruthless than, the 19th-century robber barons in the United States.
