Evidence map›Paper›PMID 37124139›Full record

ArticleHGG advances2023

Artificial intelligence-driven pan-cancer analysis reveals miRNA signatures for cancer stage prediction.

Srinivasulu Yerukala Sathipati, Ming-Ju Tsai, Sanjay K Shukla, Shinn-Ying Ho

Abstract read
In one paragraph

Article in HGG advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

25 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

4 authors.

Srinivasulu Yerukala SathipatiCenter for Precision Medicine Research, Marshfield Clinic Research Institute, Marshfield, WI 54449, USA.
Ming-Ju TsaiHinda and Arthur Marcus Institute for Aging Research at Hebrew Senior Life, Boston, MA, USA.
Sanjay K ShuklaCenter for Precision Medicine Research, Marshfield Clinic Research Institute, Marshfield, WI 54449, USA.
Shinn-Ying HoInstitute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The ability to detect cancer at an early stage in patients who would benefit from effective therapy is a key factor in increasing survivability. This work proposes an evolutionary supervised learning method called Cancer

Indexed as

MicroRNAsNeoplasmsArtificial IntelligenceBiomarkersGene Expression ProfilingHumansBiomarkersMicroRNAsArtificial IntelligenceCancer diagnosis predictioncancer early stage detectionMachine learningpan-cancer analysis

Identifiers

PMID37124139
PMCPMC10130501

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.