使用机器学习推进MS/MS光谱的预测.
Julia Nguyen1, Richard Overstreet2, Ethan King1
1Computing and Analytics Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States.
Journal of the American Society for Mass Spectrometry
|September 11, 2024
概括
使用机器学习预测质谱学光谱面临挑战. 提高准确性需要精心策划的数据集,适当的能量水平,以及与实验人员的合作,以可靠地识别小分子.
科学领域:
- 分析化学 分析化学
- 计算化学的计算化学
- 生物化学 生化学
背景情况:
- 双重质谱 (MS/MS) 对于识别小分子和代谢物至关重要.
- 目前的识别依赖于将实验光谱与参考图书馆相匹配,这些图书馆的覆盖范围有限.
- 目前正在开发in silico光谱预测方法,以扩展光谱库.
研究的目的:
- 为了调查在实现快速和准确的挑战在微小分子的MS/MS光谱预测.
- 为了解决通用机器学习基准测试在评估光谱预测算法的局限性.
- 提出提高计算光谱预测可靠性的策略.
主要方法:
- 对MS/MS光谱预测的机器学习和深度学习方法的审查和分析.
- 评估常见的基准分析实践及其对报告准确性的影响.
- 确定影响预测性能的关键因素.
主要成果:
- 一般的基准测试策略可以导致对光谱预测模型的误导性高准确度得分.
- 预测准确度受到数据集策划和适当碰撞能量的选择的影响.
- 当前的算法在预测各种小分子的光谱时面临着更大的挑战.
结论:
- 改进in silico MS/MS光谱预测需要仔细的数据整理和对碰撞能量等实验参数的考虑.
- 计算科学家和实验性质谱学家之间更紧密的合作是必不可少的.
- 需要精细的方法来克服当前的局限性,并实现真正准确的光谱预测,用于更广泛的应用.
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