Evidence map›Paper›PMID 33195426›Full record

ArticleFrontiers in molecular biosciences2020

A Theoretical Approach for Correlating Proteins to Malignant Diseases.

Rasha Elnemr, Mohammed M Nasef, Passant Elkafrawy, Mahmoud Rafea, Amani Tariq Jamal

Open access · goldAbstract read
In one paragraph

Article in Frontiers in molecular biosciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 81% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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 2 institutions in 2 countries.

Rasha ElnemrClimate Change Information Center & Renewable Energy & Expert Systems, Giza, Egypt.
Mohammed M NasefDepartment of Mathematics and Computer Science, Faculty of Science, Menoufia University, Shibin El Kom, Egypt.
Passant ElkafrawyDepartment of Mathematics and Computer Science, Faculty of Science, Menoufia University, Shibin El Kom, Egypt.
Mahmoud RafeaClimate Change Information Center & Renewable Energy & Expert Systems, Giza, Egypt.
Amani Tariq JamalDepartment of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
Menoufia University · EGKing Abdulaziz University · SA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Malignant Tumors are developed over several years due to unknown biological factors. These biological factors induce changes in the body and consequently, they lead to Malignant Tumors. Some habits and behaviors initiate these biological factors. In effect, the immune system cannot recognize a Malignant Tumor as foreign tissue. In order to discover a fascinating pattern of these habits, behaviors, and diseases and to make effective decisions, different machine learning techniques should be used. This research attempts to find the association between normal proteins (environmental factors) and diseases that are difficult to diagnose and propose justifications for those diseases. This paper proposes a technique for medical data mining using association rules. The proposed technique overcomes some of the limitations in current association algorithms such as the

Indexed as

apriori algorithmassociation ruledata miningeclat algorithmmalignant tumors

Identifiers

PMID33195426
PMCPMC7643003
OpenAlexW3093672862

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.