Evidence map›Paper›PMID 25187687›Full record

ArticleBioinformation2014

Modelling and simulation of mutant alleles of breast cancer metastasis suppressor 1 (BRMS1) gene.

Muhammad Muzammal Adeel, Muhammad Qasim, Usman Ali Ashfaq, Muhammad Shareef Masoud, Mahmood Ur Rehman, Muhammad Tahir Ul Qamar, Muhammad Rizwan Javed

Open access · hybridAbstract read
In one paragraph

Article in Bioinformation, 2014. 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
0.1field-weighted citation impact, top 45% 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, 4 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

7 authors at 1 institution in 1 country.

Muhammad Muzammal AdeelDepartment of Bioinformatics and Biotechnology, Government College University (GCUF), 38000, Faisalabad, Punjab, Pakistan.
Muhammad QasimDepartment of Bioinformatics and Biotechnology, Government College University (GCUF), 38000, Faisalabad, Punjab, Pakistan.
Usman Ali AshfaqDepartment of Bioinformatics and Biotechnology, Government College University (GCUF), 38000, Faisalabad, Punjab, Pakistan.
Muhammad Shareef MasoudDepartment of Bioinformatics and Biotechnology, Government College University (GCUF), 38000, Faisalabad, Punjab, Pakistan.
Mahmood Ur RehmanDepartment of Bioinformatics and Biotechnology, Government College University (GCUF), 38000, Faisalabad, Punjab, Pakistan.
Muhammad Tahir Ul QamarDepartment of Bioinformatics and Biotechnology, Government College University (GCUF), 38000, Faisalabad, Punjab, Pakistan.
Muhammad Rizwan JavedDepartment of Bioinformatics and Biotechnology, Government College University (GCUF), 38000, Faisalabad, Punjab, Pakistan.
Government College University, Faisalabad · PK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Computational tools occupy the prime position in the analysis of large volume of post-genomic data. These tools have advantage over the wet lab experiments in terms of high coverage, cost and time. Breast cancer is the most common cancer in females worldwide. It is a genetically heterogeneous disorder and many genes are involved in the pathway of the disease. Mutations in metastasis suppressor gene are the major cause of the disease. In this study, the effects of mutations in breast cancer metastasis suppressor 1gene upon protein structure and function were examined by means of computational tools and information from databases.This study can be useful to predict the potential effect of every allelic variant, devise new biological experiments and to interpret and predict the patho-physiological impact of new mutations or non-synonymous polymorphisms.

Indexed as

Breast cancerBRMS1Homology modelingMutation analysis

Identifiers

PMID25187687
PMCPMC4135295
OpenAlexW2088548361

What OpenQuestion holds

Textmetadata
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.