SpanSeq：基于相似性的序列数据划分方法，用于改进深度学习项目的开发与评估
SpanSeq: Similarity-based sequence data splitting method for improved development and assessment of deep learning projects
AI总结：
针对生物序列数据随机划分易引发数据泄露、导致泛化评估不可靠的问题，提出可适配多类生物序列的划分方法SpanSeq，并通过复现前沿模型开发证实该方法能规避划分偏差对评估与开发的影响。
摘要：
近年来，深度学习模型在计算生物学领域的应用大幅增长，且随着自然语言处理等领域的当前进展，这一趋势预计还将持续。这类模型虽能挖掘输入与目标之间的复杂关联，却也容易从开发所用的数据集中学习到噪声偏差。为评估其在未见过数据上的性能（即泛化能力），通常会将可用数据随机划分为开发集（训练/验证集）和测试集。尽管这一流程是标准做法，但已有研究表明，由于所用数据库中样本间存在相似性，它会产生并不可靠的泛化评估结果。在本研究中，我们提出了SpanSeq，一种面向机器学习的数据库划分方法，可适配大多数生物序列（基因、蛋白质和基因组），以避免数据集间的数据泄露。我们还通过复现两个生物信息学领域前沿模型的开发过程，探究了不限制数据集间相似性的影响，不仅证实了数据库随机划分对模型评估的负面影响，还将这类影响的范围拓展到了模型开发阶段。SpanSeq可在https://github.com/genomicepidemiology/SpanSeq获取。
英文摘要：
The use of deep learning models in computational biology has increased massively in recent years, and it is expected to continue with the current advances in the fields such as Natural Language Processing. These models, although able to draw complex relations between input and target, are also inclined to learn noisy deviations from the pool of data used during their development. In order to assess their performance on unseen data (their capacity to generalize), it is common to split the available data randomly into development (train/validation) and test sets. This procedure, although standard, has been shown to produce dubious assessments of generalization due to the existing similarity between samples in the databases used. In this work, we present SpanSeq, a database partition method for machine learning that can scale to most biological sequences (genes, proteins and genomes) in order to avoid data leakage between sets. We also explore the effect of not restraining similarity between sets by reproducing the development of two state-of-the-art models on bioinformatics, not only confirming the consequences of randomly splitting databases on the model assessment, but expanding those repercussions to the model development. SpanSeq is available at https://github.com/genomicepidemiology/SpanSeq.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
