论文标题
疾病基因鉴定的关系加权链接预测
Relation-weighted Link Prediction for Disease Gene Identification
论文作者
论文摘要
鉴定疾病基因是与疾病相关的一组基因，在理解和治愈疾病中起着重要作用。在本文中，我们提出了专门针对此问题设计的生物医学知识图，提出了一种新型的机器学习方法，该方法通过利用网络生物学和图形表示学习的最新进展来识别此类疾病基因，研究各种关系类型对预测绩效的影响，并经验上我们的算法在疾病中的竞争竞争者在24.1中均超过了竞争者的竞争性，并证明了24.1的竞争者。我们还表明，在帕金森氏病临床试验中预测药物靶标的目标鉴定的领先倡议比开放目标高于开放式目标。
Identification of disease genes, which are a set of genes associated with a disease, plays an important role in understanding and curing diseases. In this paper, we present a biomedical knowledge graph designed specifically for this problem, propose a novel machine learning method that identifies disease genes on such graphs by leveraging recent advances in network biology and graph representation learning, study the effects of various relation types on prediction performance, and empirically demonstrate that our algorithms outperform its closest state-of-the-art competitor in disease gene identification by 24.1%. We also show that we achieve higher precision than Open Targets, the leading initiative for target identification, with respect to predicting drug targets in clinical trials for Parkinson's disease.
