Name
Abstract | ||
|---|---|---|
Biomedical research results are being published at a high rate, and with existing search engines, the vast amount of published work is usually easily accessible. However, reproducing published results, either experimental data or observations is often not viable. In this work, we propose a framework to overcome some of the issues of reproducing previous research, and to ensure re-usability of published information. We present here a framework that utilizes the results from state-of-the-art biomedical literature mining, biological system modeling and analysis techniques, and provides means to scientists to assemble and reason about information from voluminous, fragmented and sometimes inconsistent literature. The overall process of automated reading, assembly and reasoning can speed up discoveries from the order of decades to the order of hours or days. Our framework described here allows for rapidly conducting thousands of in silico experiments that are designed as part of this process. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-67471-1_9 | COMPUTATIONAL METHODS IN SYSTEMS BIOLOGY (CMSB 2017) |
Keywords | Field | DocType |
Literature mining, Modeling Automation, Cancer | Search engine,Model checking,Experimental data,Computer science,Theoretical computer science,Speedup | Conference |
Volume | ISSN | Citations |
10545 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 6 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kai-Wen Liang | 1 | 0 | 3.04 |
| Qinsi Wang | 2 | 23 | 4.43 |
| Cheryl Telmer | 3 | 1 | 0.69 |
| Divyaa Ravichandran | 4 | 0 | 0.34 |
| Peter Spirtes | 5 | 616 | 101.07 |
| Natasa Miskov-Zivanov | 6 | 227 | 16.65 |