ArticleBMC genomics2015
A structure-based Multiple-Instance Learning approach to predicting in vitro transcription factor-DNA interaction.
Article in BMC genomics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 8 citations in OpenAlex.
- 4acCPred: Weakly supervised prediction ofMolecular therapy. Nucleic acids · 2022Article
- PhyloPGM: boosting regulatory function prediction accuracy using evolutionary information.Bioinformatics (Oxford, England) · 2022Article
- Weakly supervised learning of RNA modifications from low-resolution epitranscriptome data.Bioinformatics (Oxford, England) · 2021Article
- Functional Site Discovery From Incomplete Training Data: A Case Study With Nucleic Acid-Binding Proteins.Frontiers in genetics · 2019Article
- Computational modeling of in vivo and in vitro protein-DNA interactions by multiple instance learning.Bioinformatics (Oxford, England) · 2017Article
- Decoding the non-coding genome: elucidating genetic risk outside the coding genome.Genes, brain, and behavior · 2016Review
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
backgroundUnderstanding the mechanism of transcriptional regulation remains an inspiring stage of molecular biology. Recently, in vitro protein-binding microarray experiments have greatly improved the understanding of transcription factor-DNA interaction. We present a method - MIL3D - which predicts in vitro transcription factor binding by multiple-instance learning with structural properties of DNA.
resultsEvaluation on in vitro data of twenty mouse transcription factors shows that our method outperforms a method based on simple-instance learning with DNA structural properties, and the widely used k-mer counting method, for nineteen out of twenty of the transcription factors. Our analysis showed that the MIL3D approach can utilize subtle structural similarities when a strong sequence consensus is not available.
conclusionCombining multiple-instance learning and structural properties of DNA has promising potential for studying biological regulatory networks.
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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.