dorsal/arxiv
View SchemaGene Function Classification Using Bayesian Models with Hierarchy-Based Priors
| Authors | Babak Shahbaba, Radford M. Neal |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0605015 |
| URL | https://arxiv.org/abs/q-bio/0605015 |
Abstract
We investigate the application of hierarchical classification schemes to the annotation of gene function based on several characteristics of protein sequences including phylogenic descriptors, sequence based attributes, and predicted secondary structure. We discuss three Bayesian models and compare their performance in terms of predictive accuracy. These models are the ordinary multinomial logit (MNL) model, a hierarchical model based on a set of nested MNL models, and a MNL model with a prior that introduces correlations between the parameters for classes that are nearby in the hierarchy. We also provide a new scheme for combining different sources of information. We use these models to predict the functional class of Open Reading Frames (ORFs) from the E. coli genome. The results from all three models show substantial improvement over previous methods, which were based on the C5 algorithm. The MNL model using a prior based on the hierarchy outperforms both the non-hierarchical MNL model and the nested MNL model. In contrast to previous attempts at combining these sources of information, our approach results in a higher accuracy rate when compared to models that use each data source alone. Together, these results show that gene function can be predicted with higher accuracy than previously achieved, using Bayesian models that incorporate suitable prior information.
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"date_created": "2026-03-02T18:01:34.900000Z",
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"abstract": "We investigate the application of hierarchical classification schemes to the\nannotation of gene function based on several characteristics of protein\nsequences including phylogenic descriptors, sequence based attributes, and\npredicted secondary structure. We discuss three Bayesian models and compare\ntheir performance in terms of predictive accuracy. These models are the\nordinary multinomial logit (MNL) model, a hierarchical model based on a set of\nnested MNL models, and a MNL model with a prior that introduces correlations\nbetween the parameters for classes that are nearby in the hierarchy. We also\nprovide a new scheme for combining different sources of information. We use\nthese models to predict the functional class of Open Reading Frames (ORFs) from\nthe E. coli genome. The results from all three models show substantial\nimprovement over previous methods, which were based on the C5 algorithm. The\nMNL model using a prior based on the hierarchy outperforms both the\nnon-hierarchical MNL model and the nested MNL model. In contrast to previous\nattempts at combining these sources of information, our approach results in a\nhigher accuracy rate when compared to models that use each data source alone.\nTogether, these results show that gene function can be predicted with higher\naccuracy than previously achieved, using Bayesian models that incorporate\nsuitable prior information.",
"arxiv_id": "q-bio/0605015",
"authors": [
"Babak Shahbaba",
"Radford M. Neal"
],
"categories": [
"q-bio.GN"
],
"title": "Gene Function Classification Using Bayesian Models with Hierarchy-Based Priors",
"url": "https://arxiv.org/abs/q-bio/0605015"
},
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