ArticlePloS one2014
The probabilistic convolution tree: efficient exact Bayesian inference for faster LC-MS/MS protein inference.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- PepGM: a probabilistic graphical model for taxonomic inference of viral proteome samples with associated confidence scores.Bioinformatics (Oxford, England) · 2023Article
- MetaLP: An integrative linear programming method for protein inference in metaproteomics.PLoS computational biology · 2022Article
- EPIFANY: A Method for Efficient High-Confidence Protein Inference.Journal of proteome research · 2020Article
- Robust Accurate Identification and Biomass Estimates of Microorganisms via Tandem Mass Spectrometry.Journal of the American Society for Mass Spectrometry · 2020Article
- Assessing species biomass contributions in microbial communities via metaproteomics.Nature communications · 2017Article
- DeepPep: Deep proteome inference from peptide profiles.PLoS computational biology · 2017Article
- Novel Application of Junction Trees to the Interpretation of Epigenetic Differences among Lung Cancer Subtypes.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2015Article
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Abstract
Exact Bayesian inference can sometimes be performed efficiently for special cases where a function has commutative and associative symmetry of its inputs (called "causal independence"). For this reason, it is desirable to exploit such symmetry on big data sets. Here we present a method to exploit a general form of this symmetry on probabilistic adder nodes by transforming those probabilistic adder nodes into a probabilistic convolution tree with which dynamic programming computes exact probabilities. A substantial speedup is demonstrated using an illustration example that can arise when identifying splice forms with bottom-up mass spectrometry-based proteomics. On this example, even state-of-the-art exact inference algorithms require a runtime more than exponential in the number of splice forms considered. By using the probabilistic convolution tree, we reduce the runtime to O(k log(k)2) and the space to O(k log(k)) where k is the number of variables joined by an additive or cardinal operator. This approach, which can also be used with junction tree inference, is applicable to graphs with arbitrary dependency on counting variables or cardinalities and can be used on diverse problems and fields like forward error correcting codes, elemental decomposition, and spectral demixing. The approach also trivially generalizes to multiple dimensions.
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