Evidence map›Paper›PMID 37760334›Full record

ArticleAnimals : an open access journal from MDPI2023

PorcineAI-Enhancer: Prediction of Pig Enhancer Sequences Using Convolutional Neural Networks.

Ji Wang, Han Zhang, Nanzhu Chen, Tong Zeng, Xiaohua Ai, Keliang Wu

Open access · goldAbstract read
In one paragraph

Article in Animals : an open access journal from MDPI, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.2field-weighted citation impact, top 30% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 1 citations in OpenAlex.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Ji WangCollege of Animal Science and Technology, China Agricultural University, Beijing 100193, China.ORCID 0009-0009-7101-5780
Han ZhangCollege of Animal Science and Technology, China Agricultural University, Beijing 100193, China.
Nanzhu ChenInstitute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
Tong ZengCollege of Animal Science and Technology, China Agricultural University, Beijing 100193, China.
Xiaohua AiCollege of Animal Science and Technology, China Agricultural University, Beijing 100193, China.
Keliang WuCollege of Animal Science and Technology, China Agricultural University, Beijing 100193, China.
China Agricultural University · CNChinese Academy of Agricultural Sciences · CN

Funding

National Key Research and Development Program of China 2021YFF1000603National Key Research and Development Program of China 2022ZD0115704
6 · The paper itself

Abstract

Understanding the mechanisms of gene expression regulation is crucial in animal breeding. Cis-regulatory DNA sequences, such as enhancers, play a key role in regulating gene expression. Identifying enhancers is challenging, despite the use of experimental techniques and computational methods. Enhancer prediction in the pig genome is particularly significant due to the costliness of high-throughput experimental techniques. The study constructed a high-quality database of pig enhancers by integrating information from multiple sources. A deep learning prediction framework called PorcineAI-enhancer was developed for the prediction of pig enhancers. This framework employs convolutional neural networks for feature extraction and classification. PorcineAI-enhancer showed excellent performance in predicting pig enhancers, validated on an independent test dataset. The model demonstrated reliable prediction capability for unknown enhancer sequences and performed remarkably well on tissue-specific enhancer sequences.The study developed a deep learning prediction framework, PorcineAI-enhancer, for predicting pig enhancers. The model demonstrated significant predictive performance and potential for tissue-specific enhancers. This research provides valuable resources for future studies on gene expression regulation in pigs.

Indexed as

convolutional neural networksenhancersequence classification

Identifiers

PMID37760334
PMCPMC10526013
OpenAlexW4386838198

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.