dorsal/arxiv
View SchemaPrediction of transcription factor binding to DNA using rule induction methods
| Authors | Mikael Huss, Karin Nordstrom |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0505025 |
| URL | https://arxiv.org/abs/q-bio/0505025 |
Abstract
The transcription of DNA into mRNA is initiated and aided by a number of transcription factors (TFs), proteins with DNA-binding regions that attach themselves to binding sites in the DNA (transcription factor binding sites, TFBSs). As it has become apparent that both TFs and TFBSs are highly variable, tools are needed to quantify the strength of the interaction resulting from a certain TF variant binding to a certain TFBS. We used a simple way to predict interactions between protein and DNA: given experimental cases from the literature where the interaction strength between two sequences has been quantified, we created training vectors for rule induction by regarding each amino acid and nucleotide position as a single feature in the example vector. The resulting interaction strength was used as the target class or value. These training vectors were then used to build a rule induction model. We applied the rule induction method to three protein families - transcription factors from the NF-kappaB, the early-growth-response (EGR), and the paired domain groups - and their corresponding DNA targets. These three prediction problems increase in complexity with regard to model building, and thus gave us a good range for validation. The main focus of the study was the most complex problem: paired domain-DNA binding. For this problem, we also found sequence/binding strength correlations using measures from information theory. Prediction results were uniformly good: the rule induction approach was able to correctly order all of nine unseen examples for one NF-kappaB protein, achieved a correlation coefficient of 0.52 on unseen (and noisy) examples in the EGR case, and reached a classification accuracy of 69.7% for the Paired domain as evaluated by cross-validation.
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"date_created": "2026-03-02T18:01:31.809000Z",
"date_modified": "2026-03-02T18:01:31.809000Z",
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"abstract": "The transcription of DNA into mRNA is initiated and aided by a number of\ntranscription factors (TFs), proteins with DNA-binding regions that attach\nthemselves to binding sites in the DNA (transcription factor binding sites,\nTFBSs). As it has become apparent that both TFs and TFBSs are highly variable,\ntools are needed to quantify the strength of the interaction resulting from a\ncertain TF variant binding to a certain TFBS. We used a simple way to predict\ninteractions between protein and DNA: given experimental cases from the\nliterature where the interaction strength between two sequences has been\nquantified, we created training vectors for rule induction by regarding each\namino acid and nucleotide position as a single feature in the example vector.\nThe resulting interaction strength was used as the target class or value. These\ntraining vectors were then used to build a rule induction model. We applied the\nrule induction method to three protein families - transcription factors from\nthe NF-kappaB, the early-growth-response (EGR), and the paired domain groups -\nand their corresponding DNA targets. These three prediction problems increase\nin complexity with regard to model building, and thus gave us a good range for\nvalidation. The main focus of the study was the most complex problem: paired\ndomain-DNA binding. For this problem, we also found sequence/binding strength\ncorrelations using measures from information theory. Prediction results were\nuniformly good: the rule induction approach was able to correctly order all of\nnine unseen examples for one NF-kappaB protein, achieved a correlation\ncoefficient of 0.52 on unseen (and noisy) examples in the EGR case, and reached\na classification accuracy of 69.7% for the Paired domain as evaluated by\ncross-validation.",
"arxiv_id": "q-bio/0505025",
"authors": [
"Mikael Huss",
"Karin Nordstrom"
],
"categories": [
"q-bio.BM",
"q-bio.GN"
],
"title": "Prediction of transcription factor binding to DNA using rule induction methods",
"url": "https://arxiv.org/abs/q-bio/0505025"
},
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"type": "Model",
"variant": "snapshot-2026-03-01",
"version": "0.1.0"
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