ArticleBMC genomics2018
Deep learning for DNase I hypersensitive sites identification.
Article in BMC genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Benchmarking DNA foundation models for genomic and genetic tasks.Nature communications · 2025Article
- The Long-Range Chromosomal Interaction Controlling Klotho Gene Expression in Human Chronic Kidney Disease.ACS omega · 2024Article
- DNAcycP: a deep learning tool for DNA cyclizability prediction.Nucleic acids research · 2022Article
- Evaluation and application of tools for the identification of known microRNAs in plants.Applications in plant sciences · 2021Review
- Machine learning: A powerful tool for gene function prediction in plants.Applications in plant sciences · 2020Review
- Prediction of sgRNA on-target activity in bacteria by deep learning.BMC bioinformatics · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe DNase I hypersensitive sites (DHSs) are associated with the cis-regulatory DNA elements. An efficient method of identifying DHSs can enhance the understanding on the accessibility of chromatin. Despite a multitude of resources available on line including experimental datasets and computational tools, the complex language of DHSs remains incompletely understood.
methodsHere, we address this challenge using an approach based on a state-of-the-art machine learning method. We present a novel convolutional neural network (CNN) which combined Inception like networks with a gating mechanism for the response of multiple patterns and longterm association in DNA sequences to predict multi-scale DHSs in Arabidopsis, rice and Homo sapiens.
resultsOur method obtains 0.961 area under curve (AUC) on Arabidopsis, 0.969 AUC on rice and 0.918 AUC on Homo sapiens.
conclusionsOur method provides an efficient and accurate way to identify multi-scale DHSs sequences by deep learning.
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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.