From a technological aspect it's just a hard computer science problem like some of the Azure things that we're doing or some of the things that Google has had to cope with in high scale systems.
Evgeny Sogorin, stuck with a fundamental science problem at the heart of his Ph.D. at the Institute of Protein Research in Moscow.
Our framework can be used to achieve "'compressive omics," and the general theory can be readily applied to data science problems outside of biology (source code: http://gems.csail.mit.edu).edu
For example, Russ, Coffey, Hammer, and Hutchison (2009) described a case study in which a second-grade student began building her knowledge of a science problem by exploring naïve mechanistic explanations.
This century needs "Einsteins in bioinformatics", who are driven by the nature of the science problem but not its derivatives, whose approaches are not limited by disciplines, traditions, vocabularies, or even technologies.
These are data science problems, and all of this is learned natively by master teachers over the course of their careers.
It also reveals a problem that would occupy him throughout his later writings on epistemology and philosophy of science: the problem of explaining the origin of the a priori laws in human knowledge.
Like data science, the problem of communicating and sharing the information embodied in pharmacometric models is challenging.
Practical applications are based on modelling of vacuum science problems, especially the modelling of a cold cathode pressure gauge.
They have been used to provide insight into a number of vision science problems, such as comparing measurements of primate photoreceptors with receptors that are "ideal" for detecting fruit amongst foliage [18]; analysing human performance at an image classification task [19]; and modelling the colour appearance of small spots [20].
