Evidence map›Paper›PMID 32596396›Full record

ArticleBioMed research international2020

Identification of Human Enzymes Using Amino Acid Composition and the Composition of

Lifu Zhang, Benzhi Dong, Zhixia Teng, Ying Zhang, Liran Juan

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BioMed research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.8field-weighted citation impact, top 29% 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

4 citing papers in PubMed, 15 citations in OpenAlex.

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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

5 authors at 4 institutions in 1 country.

Lifu ZhangSchool of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, China.ORCID https://orcid.org/0000-0002-8329-7684
Benzhi DongInformation and Computer Engineering College, Northeast Forestry University, Harbin, China.ORCID https://orcid.org/0000-0001-7885-0820
Zhixia TengInformation and Computer Engineering College, Northeast Forestry University, Harbin, China.ORCID https://orcid.org/0000-0002-6968-4354
Ying ZhangDepartment of Pharmacy, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin, China.ORCID https://orcid.org/0000-0003-2279-8327
Liran JuanSchool of Life Science and Technology, Harbin Institute of Technology, Harbin, China.ORCID https://orcid.org/0000-0001-8826-0269
Northeast Forestry University · CNHarbin Institute of Technology · CNHeilongjiang Academy of Land Reclamation Sciences · CNUniversity of Electronic Science and Technology of China · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Enzymes are proteins that can efficiently catalyze specific biochemical reactions, and they are widely present in the human body. Developing an efficient method to identify human enzymes is vital to select enzymes from the vast number of human proteins and to investigate their functions. Nevertheless, only a limited amount of research has been conducted on the classification of human enzymes and nonenzymes. In this work, we developed a support vector machine- (SVM-) based predictor to classify human enzymes using the amino acid composition (AAC), the composition of

Indexed as

AlgorithmsAmino AcidsComputational BiologyDatabases, ProteinEnzymesHumansProteinsSupport Vector MachineAmino AcidsEnzymesProteins

Identifiers

PMID32596396
PMCPMC7273372
OpenAlexW3028865574

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.