LIDIA Group, CITIC, Universidade da Coruña, Campus de Elviña s/n, A Coruña 15071, Spain.
School of Computing, University of Kent, Canterbury CT2 7FS, United Kingdom.
Comput Biol Med. 2024 Sep;180:108999. doi: 10.1016/j.compbiomed.2024.108999. Epub 2024 Aug 12.
Dietary Restriction (DR) is one of the most popular anti-ageing interventions; recently, Machine Learning (ML) has been explored to identify potential DR-related genes among ageing-related genes, aiming to minimize costly wet lab experiments needed to expand our knowledge on DR. However, to train a model from positive (DR-related) and negative (non-DR-related) examples, the existing ML approach naively labels genes without known DR relation as negative examples, assuming that lack of DR-related annotation for a gene represents evidence of absence of DR-relatedness, rather than absence of evidence. This hinders the reliability of the negative examples (non-DR-related genes) and the method's ability to identify novel DR-related genes. This work introduces a novel gene prioritization method based on the two-step Positive-Unlabelled (PU) Learning paradigm: using a similarity-based, KNN-inspired approach, our method first selects reliable negative examples among the genes without known DR associations. Then, these reliable negatives and all known positives are used to train a classifier that effectively differentiates DR-related and non-DR-related genes, which is finally employed to generate a more reliable ranking of promising genes for novel DR-relatedness. Our method significantly outperforms (p<0.05) the existing state-of-the-art approach in three predictive accuracy metrics with up to ∼40% lower computational cost in the best case, and we identify 4 new promising DR-related genes (PRKAB1, PRKAB2, IRS2, PRKAG1), all with evidence from the existing literature supporting their potential DR-related role.
饮食限制 (DR) 是最受欢迎的抗衰老干预措施之一；最近，机器学习 (ML) 已被用于在与衰老相关的基因中识别潜在的 DR 相关基因，旨在最小化扩展我们对 DR 知识所需的昂贵的湿实验室实验。然而，为了从阳性（DR 相关）和阴性（非 DR 相关）示例中训练模型，现有的 ML 方法天真地将没有已知 DR 关系的基因标记为阴性示例，假设缺乏对基因的 DR 相关注释代表缺乏 DR 相关性的证据，而不是缺乏证据。这阻碍了阴性示例（非 DR 相关基因）的可靠性和该方法识别新的 DR 相关基因的能力。本工作介绍了一种基于两步正无标记（PU）学习范例的新型基因优先级方法：使用基于相似性的、受 KNN 启发的方法，我们的方法首先从没有已知 DR 关联的基因中选择可靠的阴性示例。然后，使用这些可靠的阴性示例和所有已知的阳性示例来训练一个分类器，该分类器能够有效地区分 DR 相关和非 DR 相关基因，最后用于生成更可靠的具有新 DR 相关性的有希望的基因排名。我们的方法在三个预测准确性指标上显著优于（p<0.05）现有的最先进方法，在最佳情况下计算成本降低了高达 ∼40%，并且我们确定了 4 个新的有希望的 DR 相关基因（PRKAB1、PRKAB2、IRS2、PRKAG1），所有这些基因都有现有文献的证据支持它们的潜在 DR 相关作用。
