Evidence map›Paper›PMID 36254306›Full record

ArticleJournal of environmental and public health2022

Construction of Mitochondrial Protection and Monitoring Model of Lon Protease Based on Machine Learning under Myocardial Ischemia Environment.

Jinliang Wang, Yang Zhang, Haijiao Shi, Ying Yang, Shuai Wang, Fengrong Wang

RetractedOpen access · hybridAbstract readRetracted Publication
In one paragraph

Article in Journal of environmental and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.1field-weighted citation impact, top 56% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Jinliang WangLiaoning University of Traditional Chinese Medicine, Shenyang 110000, China.
Yang ZhangShenyang Fourth People's Hospital of China Medical University, Shenyang 110000, China.
Haijiao ShiDepartment of Cardiology, Affiliated Hospital of Liaoning University of Traditional Chinese Medicine, Shenyang 110000, China.
Ying YangDepartment of Cardiology, Affiliated Hospital of Liaoning University of Traditional Chinese Medicine, Shenyang 110000, China.
Shuai WangDepartment of Cardiology, Affiliated Hospital of Liaoning University of Traditional Chinese Medicine, Shenyang 110000, China.
Fengrong WangDepartment of Cardiology, Affiliated Hospital of Liaoning University of Traditional Chinese Medicine, Shenyang 110000, China.ORCID 0000-0002-4229-0590
Liaoning University of Traditional Chinese Medicine · CNFourth Affiliated Hospital of China Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The localization of a protein's submitochondrial structure is important for therapeutic design of associated disorders caused by mitochondrial abnormalities because many human diseases are directly tied to mitochondria. When Lon protease expression changes, glycolysis replaces respiratory metabolism in the cell, which is a common occurrence in cancer cells. The fact that protein formation is a dynamic research object makes it impossible to reproduce the unique living environment of proteins in an experimental setting, which surely makes it more challenging to determine protein function through experiments. This research suggests a model of Lon protease-based mitochondrial protection under myocardial ischemia based on ML (machine learning). To ensure the balance of all submitochondrial proteins, the data set is processed using a random oversampling method, each overlapping fixed-length subsequence that is created from the protein sequence functions as a channel in the convolution layer. The results demonstrate that applying the oversampling strategy increases the ROC value by 17.6%-21.3%. Our prediction method is successful as evidenced by the fact that ML prediction outperforms the predictions of other conventional classifiers.

Indexed as

Myocardial IschemiaProtease LaATP-Dependent ProteasesHumansMachine LearningMitochondriaATP-Dependent ProteasesProtease La

Identifiers

PMID36254306
PMCPMC9569194
OpenAlexW4303832658

What OpenQuestion 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 OpenQuestion graph.