Evidence map›Paper›PMID 40149396›Full record

ReviewGenes2025

Multiomics with Evolutionary Computation to Identify Molecular and Module Biomarkers for Early Diagnosis and Treatment of Complex Disease.

Han Cheng, Mengyu Liang, Yiwen Gao, Wenshan Zhao, Wei-Feng Guo

Abstract readReview
In one paragraph

Review in Genes, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Review
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.

Han ChengSchool of Life Sciences, Zhengzhou University, Zhengzhou 450001, China.
Mengyu LiangSchool of Life Sciences, Zhengzhou University, Zhengzhou 450001, China.
Yiwen GaoSchool of Life Sciences, Zhengzhou University, Zhengzhou 450001, China.
Wenshan ZhaoSchool of Life Sciences, Zhengzhou University, Zhengzhou 450001, China.
Wei-Feng GuoSchool of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China.ORCID 0000-0003-0565-177X

Funding

National Natural Science Foundation of China 62476253Natural Science Foundation of Henan Province 242300421401Natural Science Foundation of Henan Province 242301420078Open Research Fund of State Key Laboratory of Digital Medical Engineering 2024-K07
6 · The paper itself

Abstract

It is important to identify disease biomarkers (DBs) for early diagnosis and treatment of complex diseases in personalized medicine. However, existing methods integrating intelligence technologies and multiomics to predict key biomarkers are limited by the complex dynamic characteristics of omics data, making it difficult to meet the high-precision requirements for biomarker characterization in large dimensions. This study reviewed current analysis methods of evolutionary computation (EC) by considering the essential characteristics of DB identification problems and the advantages of EC, aiming to explore the complex dynamic characteristics of multiomics. In this study, EC-based biomarker identification strategies were summarized as evolutionary algorithms, swarm intelligence and other EC methods for molecular and module DB identification, respectively. Finally, we pointed out the challenges in current research and future research directions. This study can enrich the application of EC theory and promote interdisciplinary integration between EC and bioinformatics.

Indexed as

BiomarkersComputational BiologyGenomicsAlgorithmsEarly DiagnosisHumansMultiomicsPrecision MedicineBiomarkersbiomarkerscomplex diseaseevolutionary computationmultiomics

Identifiers

PMID40149396
PMCPMC11942451

What Socratic holds

Textmetadata
LicenceCC BY
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.