Deep vs. Shallow Learning-based Filters of MSMS Spectra in Support of Protein Search Engines.
Deep vs. Shallow Learning-based Filters of MSMS Spectra in Support of Protein Search Engines.
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深度与基于浅层学习的MSMS光谱过滤器支持蛋白质搜索引擎。
DOI：
10.1109/bibm.2017.8217824
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发表时间：
2017-11
期刊：
影响因子：
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通讯作者：
Gupta A
中科院分区：
文献类型：
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作者：
Maabreh M;Qolomany B;Springstead J;Alsmadi I;Gupta A
尽管观测到的光谱数量与搜索时间之间存在线性关系，但当前的蛋白质搜索引擎，即使是并行版本，也可能需要几个小时来搜索大量可以在短时间内生成的MSMS光谱。经过艰苦的搜索过程后，一些（有时是大多数）观察到的光谱被标记为不可识别。我们评估了机器学习在构建高效 MSMS 过滤器以去除不可识别光谱方面的作用。我们使用 9 种不同配置的浅层学习算法对深度学习算法进行比较和评估。使用从两个不同的搜索引擎、不同的仪器、不同的大小和不同的物种生成的 10 个不同的数据集，我们通过实验证明深度学习模型在过滤 MSMS 谱方面非常强大。我们还表明，我们的简单特征列表非常重要，而其他浅层学习算法在过滤 MSMS 谱方面显示出令人鼓舞的结果。我们的深度学习模型可以排除大约 50% 的不可识别光谱，而平均仅丢失 9% 的可识别光谱。至于浅层学习，随机森林、支持向量机和神经网络等算法显示出令人鼓舞的结果，平均消除了 70% 的不可识别光谱，同时丢失了约 25% 的可识别光谱。在感兴趣的蛋白质处于较低细胞或组织浓度的情况下，深度学习算法可能尤其更有用，而其他算法可能对于浓缩或更高表达的蛋白质更有用。
Despite the linear relation between the number of observed spectra and the searching time, the current protein search engines, even the parallel versions, could take several hours to search a large amount of MSMS spectra, which can be generated in a short time. After a laborious searching process, some (and at times, majority) of the observed spectra are labeled as non-identifiable. We evaluate the role of machine learning in building an efficient MSMS filter to remove non-identifiable spectra. We compare and evaluate the deep learning algorithm using 9 shallow learning algorithms with different configurations. Using 10 different datasets generated from two different search engines, different instruments, different sizes and from different species, we experimentally show that deep learning models are powerful in filtering MSMS spectra. We also show that our simple features list is significant where other shallow learning algorithms showed encouraging results in filtering the MSMS spectra. Our deep learning model can exclude around 50% of the non-identifiable spectra while losing, on average, only 9% of the identifiable ones. As for shallow learning, algorithms of: Random Forest, Support Vector Machine and Neural Networks showed encouraging results, eliminating, on average, 70% of the non-identifiable spectra while losing around 25% of the identifiable ones. The deep learning algorithm may be especially more useful in instances where the protein(s) of interest are in lower cellular or tissue concentration, while the other algorithms may be more useful for concentrated or more highly expressed proteins.