ArticleJournal of proteome research2020
Machine Learning Strategy That Leverages Large Data sets to Boost Statistical Power in Small-Scale Experiments.
Article in Journal of proteome research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Artificial Intelligence and Machine Learning in Mass Spectrometry-Based Proteomics and Metabolomics.Methods in molecular biology (Clifton, N.J.) · 2027Article
- Synchronized long-read genome, methylome, epigenome and transcriptome profiling resolve a Mendelian condition.Nature genetics · 2025Article
- STR mutations on chromosome 15q cause thyrotropin resistance by activating a primate-specific enhancer of MIR7-2/MIR1179.Nature genetics · 2024Article
- Unsupervised learning analysis on the proteomes of Zika virus.PeerJ. Computer science · 2024Article
- Article
- Generalized precursor prediction boosts identification rates and accuracy in mass spectrometry based proteomics.Communications biology · 2023Article
- Challenges and Opportunities for Single-cell Computational Proteomics.Molecular & cellular proteomics : MCP · 2023Review
- Driving Single Cell Proteomics Forward with Innovation.Journal of proteome research · 2021Article
- Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics.Nature communications · 2021Article
- mokapot: Fast and Flexible Semisupervised Learning for Peptide Detection.Journal of proteome research · 2021Article
- Proteome Discoverer-A Community Enhanced Data Processing Suite for Protein Informatics.Proteomes · 2021Review
- Optimization of skeletal protein preparation for LC-MS/MS sequencing yields additional coral skeletal proteins inBMC materials · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Machine learning methods have proven invaluable for increasing the sensitivity of peptide detection in proteomics experiments. Most modern tools, such as Percolator and PeptideProphet, use semisupervised algorithms to learn models directly from the data sets that they analyze. Although these methods are effective for many proteomics experiments, we suspected that they may be suboptimal for experiments of smaller scale. In this work, we found that the power and consistency of Percolator results were reduced as the size of the experiment was decreased. As an alternative, we propose a different operating mode for Percolator: learn a model with Percolator from a large data set and use the learned model to evaluate the small-scale experiment. We call this a "static modeling" approach, in contrast to Percolator's usual "dynamic model" that is trained anew for each data set. We applied this static modeling approach to two settings: small, gel-based experiments and single-cell proteomics. In both cases, static models increased the yield of detected peptides and eliminated the model-induced variability of the standard dynamic approach. These results suggest that static models are a powerful tool for bringing the full benefits of Percolator and other semisupervised algorithms to small-scale experiments.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.