ArticlePeerJ2019
TransPrise: a novel machine learning approach for eukaryotic promoter prediction.
Article in PeerJ, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 21 citations in OpenAlex.
- TEITbase: a database for transposable element (TE)-initiated transcripts in human cancers.Database : the journal of biological databases and curation · 2026Article
- Bioinformatics in Russia: history and present-day landscape.Briefings in bioinformatics · 2024Review
- Deep learning and support vector machines for transcription start site identification.PeerJ. Computer science · 2023Article
- Nonlinear physics opens a new paradigm for accurate transcription start site prediction.BMC bioinformatics · 2022Article
- Annotating TSSs in Multiple Cell Types Based on DNA Sequence and RNA-seq Data via DeeReCT-TSS.Genomics, proteomics & bioinformatics · 2022Article
- Genome-Wide Prediction of Transcription Start Sites in Conifers.International journal of molecular sciences · 2022Article
- Genomics enters the deep learning era.PeerJ · 2022Review
- TSSFinder-fast and accurate ab initio prediction of the core promoter in eukaryotic genomes.Briefings in bioinformatics · 2021Article
- Article
- Genome annotation across species using deep convolutional neural networks.PeerJ. Computer science · 2020Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As interest in genetic resequencing increases, so does the need for effective mathematical, computational, and statistical approaches. One of the difficult problems in genome annotation is determination of precise positions of transcription start sites. In this paper we present TransPrise-an efficient deep learning tool for prediction of positions of eukaryotic transcription start sites. Our pipeline consists of two parts: the binary classifier operates the first, and if a sequence is classified as TSS-containing the regression step follows, where the precise location of TSS is being identified. TransPrise offers significant improvement over existing promoter-prediction methods. To illustrate this, we compared predictions of TransPrise classification and regression models with the TSSPlant approach for the well annotated genome of
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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.