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Summary With genomic data skyrocketing, their biological interpretation remains a serious challenge. Diverse computational methods address this problem by pointing to the existence of recurrent patterns among sequence, structure, and function. These patterns emerge naturally from evolutionary variation, natural selection, and divergence—the defining features of biological systems—and they identify molecular events and shapes that underlie specificity of function and allosteric communication. Here we review these methods, and the patterns they identify in case studies and in proteome-wide applications, to infer and rationally redesign function.

SignificanceWe adapted natural language processing to the biological literature and demonstrated end-to-end automated knowledge discovery by exploring subtle word connections. General text mining scanned 21 million publication abstracts and selected a reliable 130,000 from which hypothesis generation algorithms predicted kinases not known to phosphorylate p53, but likely to do so. Six of these p53 kinase candidates passed experimental validation. Among them NEK2 was examined in depth and shown to repress p53 and promote cell division. This work demonstrates the possibility of integrating a vast corpora of written knowledge to compute valuable hypotheses that will often test true and fuel discovery.

We present KnIT, the Knowledge Integration Toolkit, a system for accelerating scientific discovery and predicting previously unknown protein-protein interactions. Such predictions enrich biological research and are pertinent to drug discovery and the understanding of disease. Unlike a prior study, KnIT is now fully automated and demonstrably scalable. It extracts information from the scientific literature, automatically identifying direct and indirect references to protein interactions, which is knowledge that can be represented in network form. It then reasons over this network with techniques such as matrix factorization and graph diffusion to predict new, previously unknown interactions. The accuracy and scope of KnIT's knowledge extractions are validated using comparisons to structured, manually curated data sources as well as by performing retrospective studies that predict subsequent literature discoveries using literature available prior to a given date. The KnIT methodology is a step towards automated hypothesis generation from text, with potential application to other scientific domains.

A Nintendo® Wiimote® enabled testing of both zero-and first-order of control for a Fitts' Lawstyle pointing task using the same device. The Wiimote® differs from standard computer input devices in that the user has available a full range of three-dimensional motions. Participants were assigned to one of the two orders of control and completed a pointing task that included 50 trials on three sets of boxes, each a different size and distance from each other. Results indicated that participants using the Wiimote® as a zero-order input device (i.e., directly controlling cursor position) were roughly 2.5 times faster at completing the task than those using the Willmote® as a first-order device, (i.e., controlling cursor velocity). As expected, participants using the firstorder controller had smaller effective distances than those using the zero-order control scheme. Surprisingly, no meaningful differences were found between the two groups for overall error rate. This raises interesting questions for the future of three-dimensional control devices.