Standigm Inc., 70, Nonhyeon-ro 85-gil, Gangnam-gu, Seoul, 06234, Republic of Korea.
Standigm UK Co., Ltd, 50-60 Station Road, Cambridge, CB1 2JH, UK.
BMC Bioinformatics. 2023 Mar 21;24(1):105. doi: 10.1186/s12859-023-05227-x.
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.
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.
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.
从无偏的高通量数据中提取有意义的信息是各个领域的一个挑战。具体来说，在药物发现的早期阶段，为了了解疾病生物学，需要生成大量数据以确定疾病靶点。已经应用了几种基于随机游走的方法来解决这个问题，但它们仍然存在局限性。因此，我们建议使用一种新的方法，通过随机游走来增强高通量数据分析的效果。
我们开发了一种新的基于随机游走的算法，名为带有扭曲网络的优先级算法（PWN），它使用扭曲网络来实现增强的性能。网络扭曲基于内部和外部特征：图曲率和先验知识。
我们表明，当应用于随机游走算法时，这些组合特征协同增加了最终的性能，这使得 PWN 在几种其他已知方法中始终能够实现最佳性能。此外，我们进行了后续实验来分析 PWN 的特征。
