Evidence map›Paper›PMID 34447850›Full record

ArticleBioMed research international2021

A Machine Learning Study on the Thermostability Prediction of (R)-

Li-Li Jia, Ting-Ting Sun, Yan Wang, Yu Shen

Open access · hybridAbstract read
In one paragraph

Article in BioMed research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.7field-weighted citation impact, top 31% 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

3 citing papers in PubMed, 12 citations in OpenAlex.

  1. Article
  2. Review
  3. 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

4 authors at 1 institution in 1 country.

Li-Li JiaSchool of Science, School of Big Data, Zhejiang University of Science and Technology, Hangzhou 310008, China.
Ting-Ting SunSchool of Science, School of Big Data, Zhejiang University of Science and Technology, Hangzhou 310008, China.ORCID https://orcid.org/0000-0003-1388-3458
Yan WangSchool of Science, School of Big Data, Zhejiang University of Science and Technology, Hangzhou 310008, China.
Yu ShenSchool of Science, School of Big Data, Zhejiang University of Science and Technology, Hangzhou 310008, China.ORCID https://orcid.org/0000-0002-3409-2412
Zhejiang University of Science and Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence technologies such as machine learning have been applied to protein engineering, with unique advantages in protein structure, function prediction, catalytic activity, and other issues in recent years. Screening better mutants is still a bottleneck in protein engineering. In this paper, a new sequence-activity relationship method was analyzed for its application in improving the thermal stability of Aspergillus terreus (R)-

Indexed as

Machine LearningAminesAspergillusHot TemperatureModels, MolecularProtein EngineeringProtein StabilityProtein Structural ElementsSubstrate SpecificityTransaminasesAminesTransaminases

Identifiers

PMID34447850
PMCPMC8384528
OpenAlexW3194316736

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.