In the past few years, the prediction models have shown remarkable performance in most biological correlation prediction tasks. These tasks traditionally use a fixed dataset, and the model, once trained, is deployed as is. These models often encounter training issues such as sensitivity to hyperparameter tuning and “catastrophic forgetting” when adding new data. However, with the development of biomedicine and the accumulation of biological data, new predictive models are required to face the challenge of adapting to change. To this end, we propose a computational approach based on Broad learning system (BLS) to predict potential disease-associated miRNAs that retain the ability to distinguish prior training associations when new data need to be adapted. In particular, we are introducing incremental learning to the field of biological association prediction for the first time and proposed a new method for quantifying sequence similarity. In the performance evaluation, the AUC in the 5-fold cross-validation was 0.9400 +/- 0.0041. To better assess the effectiveness of MISSIM, we compared it with various classifiers and former prediction models. Its performance is superior to the previous method. Besides, the case study on identifying miRNAs associated with breast neoplasms, lung neoplasms and esophageal neoplasms show that 34, 36 and 35 out of the top 40 associations predicted by MISSIM are confirmed by recent biomedical resources. These results provide ample convincing evidence of this approach have potential value and prospect in promoting biomedical research productivity.

中文翻译：

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MISSIM：一种基于增量学习的模型，可用于预测 miRNA 疾病关联

在过去的几年中，预测模型在大多数生物相关性预测任务中都表现出了显着的性能。这些任务传统上使用固定的数据集，模型一旦训练，就会按原样部署。这些模型在添加新数据时经常会遇到训练问题，例如对超参数调整的敏感性和“灾难性遗忘”。然而，随着生物医学的发展和生物数据的积累，需要新的预测模型来应对适应变化的挑战。为此，我们提出了一种基于广泛学习系统 (BLS) 的计算方法来预测潜在的疾病相关 miRNA，这些 miRNA 在需要调整新数据时保留区分先前训练关联的能力。特别是，我们首次将增量学习引入生物关联预测领域，并提出了一种量化序列相似性的新方法。在性能评估中，5 折交叉验证中的 AUC 为 0.9400 +/- 0.0041。为了更好地评估 MISSIM 的有效性，我们将其与各种分类器和以前的预测模型进行了比较。其性能优于前一种方法。此外，关于识别与乳腺肿瘤、肺肿瘤和食管肿瘤相关的 miRNA 的案例研究表明，在 MISSIM 预测的前 40 种关联中，有 34、36 和 35 个被最近的生物医学资源证实。这些结果提供了充分的令人信服的证据，证明这种方法在促进生物医学研究生产力方面具有潜在价值和前景。