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. 2017 Nov:2017:1175-1182.
doi: 10.1109/BIBM.2017.8217824. Epub 2017 Dec 18.

Deep vs. Shallow Learning-based Filters of MSMS Spectra in Support of Protein Search Engines

Majdi Maabreh  1 , Basheer Qolomany  1 , James Springstead  2 , Izzat Alsmadi  3 , Ajay Gupta  1
Affiliations

Deep vs. Shallow Learning-based Filters of MSMS Spectra in Support of Protein Search Engines

Majdi Maabreh et al. Proceedings (IEEE Int Conf Bioinformatics Biomed). 2017 Nov.

Abstract

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.

Keywords: Big Data; Deep Learning; MSMS Filters; Machine Learning; Protein Search Engine; Searching Space Optimization; Shallow Learning.

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Figures

Figure 1.
Figure 1.
The performance of machine learning algorithms using Human01-pFind dataset
Figure 2.
Figure 2.
The performance of machine learning algorithms using Human01-Comet dataset
Figure 3.
Figure 3.
The performance of machine learning algorithms using Human06-pFind dataset
Figure 4.
Figure 4.
The performance of machine learning algorithms using Human06-Comet dataset
Figure 5.
Figure 5.
The performance of machine learning algorithms using Human05-Comet dataset.
Figure 6.
Figure 6.
The performance of machine learning algorithms using Mouse-pFind dataset.
Figure 7.
Figure 7.
The performance of machine learning algorithms using Mouse-Comet dataset
Figure 8.
Figure 8.
The performance of machine learning algorithms using Soybean-pFind dataset
Figure 9.
Figure 9.
The performance of machine learning algorithms using Soybean-Comet dataset
Figure 10.
Figure 10.
The performance of machine learning algorithms using Rat-Comet dataset
Figure 11.
Figure 11.
Average performance of various machine learning algorithms across all datasets in our experiment
See this image and copyright information in PMC

References

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