Name
Abstract | ||
|---|---|---|
Ovarian cancer (OVC) is one of the deadliest cancers in women. At present, effective clinical therapy for OVC is still limited. We adopted a computational method to identify OVC candidate genes based on subnetwork extraction algorithm in the protein interaction network. Of the final identified five genes, MSH3 and PIK3R3 genes have been reported to be implicated in OVC tumorigenesis, indicating that the method is effective, whereas the other three genes (MAPK8IP1, PICK1, and IQGAP3) might have potential roles in OVC development. The results provided a novel insight into identifying the OVC-related genes and exploring new methods for OVC therapy. |
Year | Venue | Field |
|---|---|---|
2015 | ICIC | MSH3,Gene,Computer science,Artificial intelligence,Ovarian cancer,Computational biology,Carcinogenesis,Candidate gene,Interaction network,Bioinformatics,Interaction information,Subnetwork,Machine learning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
4 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Di Zhang | 1 | 2 | 2.75 |
| Qingbao Wang | 2 | 0 | 1.01 |
| Zhu Rongrong | 3 | 7 | 1.81 |
| Hai-Tao Li | 4 | 0 | 0.34 |
| Chun-Hou Zheng | 5 | 25 | 3.91 |
| Junfeng Xia | 6 | 22 | 4.04 |