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| Content Provider | Springer Nature : BioMed Central |
|---|---|
| Author | Han, Seokjin Hong, Jinhee Yun, So Jeong Koo, Hee Jung Kim, Tae Yong |
| Abstract | Background Extracting meaningful information from unbiased high-throughput data has been a challenge in diverse areas. Specifically, in the early stages of drug discovery, a considerable amount of data was generated to understand disease biology when identifying disease targets. Several random walk-based approaches have been applied to solve this problem, but they still have limitations. Therefore, we suggest a new method that enhances the effectiveness of high-throughput data analysis with random walks. Results We developed a new random walk-based algorithm named prioritization with a warped network (PWN), which employs a warped network to achieve enhanced performance. Network warping is based on both internal and external features: graph curvature and prior knowledge. Conclusions We showed that these compositive features synergistically increased the resulting performance when applied to random walk algorithms, which led to PWN consistently achieving the best performance among several other known methods. Furthermore, we performed subsequent experiments to analyze the characteristics of PWN. |
| Related Links | https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/s12859-023-05227-x.pdf |
| Ending Page | 21 |
| Page Count | 21 |
| Starting Page | 1 |
| File Format | HTM / HTML |
| ISSN | 14712105 |
| DOI | 10.1186/s12859-023-05227-x |
| Journal | BMC Bioinformatics |
| Issue Number | 1 |
| Volume Number | 24 |
| Language | English |
| Publisher | BioMed Central |
| Publisher Date | 2023-03-21 |
| Access Restriction | Open |
| Subject Keyword | Bioinformatics Microarrays Computational Biology Computer Appl. in Life Sciences Algorithms Disease-target identification Protein–protein interaction Random walk Machine learning Computational Biology/Bioinformatics |
| Content Type | Text |
| Resource Type | Article |
| Subject | Molecular Biology Biochemistry Computer Science Applications Applied Mathematics Structural Biology |
| Journal Impact Factor | 2.9/2023 |
| 5-Year Journal Impact Factor | 3.6/2023 |
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