论文标题
使用综合人类遗传学证据来预测临床结果的贝叶斯张量分解
Bayesian tensor factorization for predicting clinical outcomes using integrated human genetics evidence
论文作者
论文摘要
由于安全性和功效，候选药物的批准成功率非常低。越来越可用的有关目标，药物分子和适应症的高维信息为ML方法提供了整合多种数据模式并更好地预测临床上有希望的药物靶标的机会。值得注意的是，具有人类遗传学证据的药物靶标被证明具有更好的成功率。但是，最近基于张力分解的方法发现，有关目标和指示的其他信息可能不一定提高预测精度。在这里，我们通过整合从公共可用来源整合的不同类型的人类遗传学证据来支持每个目标指示对来重新审视这种方法。我们使用贝叶斯张量分解来表明，结合了所有可用的人类遗传学证据（稀有疾病，基因负担，常见疾病）的模型可以适度地改善使用单线遗传学证据而不是模型的临床结果预测。我们为预测临床结局的成功的不同类型的人类遗传学证据的相对预测能力提供了更多的见解。
The approval success rate of drug candidates is very low with the majority of failure due to safety and efficacy. Increasingly available high dimensional information on targets, drug molecules and indications provides an opportunity for ML methods to integrate multiple data modalities and better predict clinically promising drug targets. Notably, drug targets with human genetics evidence are shown to have better odds to succeed. However, a recent tensor factorization-based approach found that additional information on targets and indications might not necessarily improve the predictive accuracy. Here we revisit this approach by integrating different types of human genetics evidence collated from publicly available sources to support each target-indication pair. We use Bayesian tensor factorization to show that models incorporating all available human genetics evidence (rare disease, gene burden, common disease) modestly improves the clinical outcome prediction over models using single line of genetics evidence. We provide additional insight into the relative predictive power of different types of human genetics evidence for predicting the success of clinical outcomes.
