Evidence map›Paper›PMID 32273881›Full record

ArticleFrontiers in genetics2020

Computing Skin Cutaneous Melanoma Outcome From the HLA-Alleles and Clinical Characteristics.

Anjali Dhall, Sumeet Patiyal, Harpreet Kaur, Sherry Bhalla, Chakit Arora, Gajendra P S Raghava

Open access · goldAbstract read
In one paragraph

Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
2.9field-weighted citation impact, top 7% 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

19 citing papers in PubMed, 40 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

6 authors at 1 institution in 1 country.

Anjali DhallDepartment of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.
Sumeet PatiyalDepartment of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.
Harpreet KaurDepartment of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.
Sherry BhallaDepartment of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.
Chakit AroraDepartment of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.
Gajendra P S RaghavaDepartment of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.
Indraprastha Institute of Information Technology Delhi · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Human leukocyte antigen (HLA) are essential components of the immune system that stimulate immune cells to provide protection and defense against cancer. Thousands of HLA alleles have been reported in the literature, but only a specific set of HLA alleles are present in an individual. The capability of the immune system to recognize cancer-associated mutations depends on the presence of a particular set of alleles, which elicit an immune response to fight against cancer. Therefore, the occurrence of specific HLA alleles affects the survival outcome of cancer patients. In the current study, prediction models were developed, using 401 cutaneous melanoma patients, to predict the overall survival (OS) of patients using their clinical data and HLA alleles. We observed that the presence of certain favorable superalleles like HLA-B

Indexed as

cutaneous melanomaHazard ratioHLAmachine learningprognosisregressionsuperallelessurvival analysis

Identifiers

PMID32273881
PMCPMC7113398
OpenAlexW3013274818

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.