评估分子数据集的人工智能模型的通用性.
Yasha Ektefaie1, Andrew Shen1,2, Daria Bykova3
1Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
bioRxiv : the preprint server for biology
|March 11, 2024
概括
深度学习模型很难对未见的分子序列进行概括. 一个新的框架Spectra揭示了当前的基准错误地描述了可概括性,因为它没有考虑序列重叠,突出了需要更好的评估方法的需要.
科学领域:
- 计算生物学 计算生物学
- 机器学习 机器学习
- 基因组学就是基因组学.
背景情况:
- 深度学习模型擅长分子测序数据,但它们对新型序列的概括性不明.
- 使用基于元数据 (MB) 或基于序列相似性 (SB) 的分割的现有基准没有捕捉到交叉分割重叠的全部频谱,可能导致模型性能的错误描述.
结论:
- 在生物序列建模中,Spectra提供了对深度学习模型概括性的更全面的评估.
- 这些发现强调了在基准设计中考虑序列相似性和重叠的重要性.
- 这项工作有助于我们更好地理解基础模型如何在生物应用中进行概括.
更多相关视频
相关概念视频
02:54Evolutionary Relationships through Genome Comparisons
5.7K
Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
5.7K
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
40
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
40