![](bg4.png)
new drug candidate. Then, a target’s indication of dis-
ease will marshal on the z-axis from diseases of initial
concern to other pathologically highly-connected dis-
eases.
Therefore, these axes create a unique space for the
evolving target inventory. Each data item is drawn as
a cube on those axes so that a set of cubes can be per-
ceived as a big cube being built up like Lego blocks.
The solidness and volume of the big cube can convey
the feeling of completeness and richness of validated
druggable targets. A cube has three non-geometric
properties, transparency, hue, and brightness which
represent the degree of progress, the kind of diseases,
and the sort of targets, respectively. These hue and
brightness can function as the referential cue when
translating and rotating the entire cube. Figure 1 de-
picts an example 3D atlas for drug target discovery.
5 CONCLUSIONS
The problem itself is simple. The entire image of the
R&D scene is needed and should be generated based
on real data. The constraint is that the image ought
to be geometrically informative to exploit human spa-
tial perception. Thus the properties of the information
axes are defined so that a collection of data items can
appear as a geometric shape. To appreciate and re-
member the shape concretely, a referential cue is sug-
gested to be an anchor point in the information space
in which the user can often be confused or lost.
The suggested prototype has partly shown the
proof of concept for the drug target discovery process
in 3D space. But the information axes of the 3D atlas
should be elaborated to be more commensurable and
interpolatable. Finally, to more facilitate the user’s
spatial perception, motion guidance for inspecting the
3D atlas such as navigational paths along the patho-
logical connections will be developed.
ACKNOWLEDGEMENTS
This research was supported by Global Frontier Re-
search Grant NRF-M1AXA002-2010-0029785 of the
National Research Foundation funded by the Ministry
of Education, Science and Technology of Korea.
REFERENCES
Beltrame, L., Calura, E., Popovici, R. R., Rizzetto, L.,
Guedez, D. R., Donato, M., Romualdi, C., Draghici,
S., and Cavalieri, D. (2011). The biological connec-
tion markup language: a sbgn-compliant format for
visualization, filtering and analysis of biological path-
ways. Bioinformatics, 27(15):2127–2133.
Campbell, S. J., Gaulton, A., Marshall, J., Bichko, D., Mar-
tin, S., Brouwer, C., and Harland, L. (2010). Visualiz-
ing the drug target landscape. Drug Discovery Today,
15(1/2):3–15.
Jussi Paananen, G. W. (2009). Forg3d: Force-directed 3d
graph editor for visualization of integrated genome
scale data. BMC Systems Biology, 3:26.
Kim, S. K., Lund, J., Kiraly, M., Duke, K., Jiang, M., Stu-
art, J. M., Eizinger, A., Wylie, B. N., and Davidson,
G. S. (2001). A gene expression map for caenorhab-
ditis elegans. Science, 293:2087–2092.
Novre, N. L., Hucka, M., Mi, H., Moodie, S., Schreiber,
F., Sorokin, A., Demir, E., Wegner, K., Aladjem,
M. I., Wimalaratne, S. M., Bergman, F. T., Gauges,
R., Ghazal, P., Kawaji, H., Li, L., Matsuoka, Y., Vill-
ger, A., Boyd, S. E., Calzone, L., Courtot, M., Do-
grusoz, U., Freeman, T. C., Funahashi, A., Ghosh, S.,
Jouraku, A., Kim, S., Kolpakov, F., Luna, A., Sahle,
S., Schmidt, E., Watterson, S., Wu, G., Goryanin,
I., Kell, D. B., Sander, C., Sauro, H., Snoep, J. L.,
Kohn, K., and Kitano, H. (2009). The systems biology
graphical notation. Nature Biotechnology, 27:735–
741.
Pavlopoulos, G. A., O’Donoghue, S. I., Satagopam, V. P.,
Soldatos, T. G., Pafilis, E., and Schneider, R. (2008).
Arena3d: visualization of biological networks in 3d.
BMC Systems Biology, 2:104.
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang,
J. T., Ramage, D., Amin, N., Schwikowski, B., and
Ideker, T. (2003). Cytoscape: A software environment
for integrated models of biomolecular interaction net-
works. Genome Research, 13:2498–2504.
Widjaja, Y. Y., Pang, C. N. I., Li, S. S., Wilkins, M. R., and
Lambert, T. D. (2009). The interactorium: visualis-
ing proteins, complexes and interaction networks in a
virtual 3d cell. Proteomics, 9(23):5309–5315.
You, Q., Fang, S., and Chen, J. Y. (2008). Geneterrain:
visual exploration of differential gene expression pro-
files organized in native biomolecular interaction net-
works. Information Visualization, 9:1–12.
LANDSCAPING THE FRAMEWORK OF BIO RESEARCH PROJECT - Generation of the 3D Atlas for Drug Target
Discovery
359