Evidence map›Paper›PMID 40413362›Full record

ArticleDiscover oncology2025

Big data-driven machine learning: transforming multi-omics lung cancer research.

Yanqi Zhang, Mingyu Liu, Jinhua Luo, Zhongqing Xu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. Article
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.

Yanqi Zhang *Department of General Practice, Tongren Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200336, China.
Mingyu Liu *Department of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 211166, China.
Jinhua LuoDepartment of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 211166, China. ljhua19661220@163.com.
Zhongqing XuDepartment of General Practice, Tongren Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200336, China. zhongqing_xu@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLung cancer remains a major global health threat, with its biological complexity and patient heterogeneity posing significant challenges. Novel machine learning approaches now offer effective tools to interpret complex biological information hierarchies, showing promise to transform lung cancer treatment approaches.

methodsWe analyzed comprehensive biological datasets from TCGA and other databases, integrating DNA, RNA, miRNA, protein, and metabolite information. Multiple machine learning methods were employed to build diagnostic tools, treatment response predictors, and survival estimation models.

resultsOur machine learning approaches effectively distinguished cancer patients from healthy controls. Analysis identified unique molecular characteristics between lung cancer subtypes and discovered biomarkers that help predict treatment efficacy and patient prognosis. Adding clinical data to biological information significantly improved model accuracy and enhanced patient stratification.

conclusionThis study marks significant progress toward precision cancer therapy by demonstrating how machine learning can help decode the complex biology of lung cancer.

Indexed as

DiagnosisLung cancerMachine learningMulti-omicsPrognosisTreatment

Identifiers

PMID40413362
PMCPMC12103408

What Socratic 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 Socratic graph.