ArticleGenes2025
Optimizing Model Performance and Interpretability: Application to Biological Data Classification.
Zhenyu Huang, Xuechen Mu, Yangkun Cao, Qiufen Chen, Siyu Qiao, Bocheng Shi, Gangyi Xiao, Yan Wang, Ying Xu
Abstract read
In one paragraphArticle in Genes, 2025. 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
–field-weighted citation impact
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 registryThe 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 literatureWho cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what moneyAuthors and funding
9 authors.
Xuechen MuSystems Biology Lab for Metabolic Reprogramming, Department of Human Genetics and Cell Biology, School of Medicine, Southern University of Science and Technology, Shenzhen 518055, China.
Qiufen ChenSystems Biology Lab for Metabolic Reprogramming, Department of Human Genetics and Cell Biology, School of Medicine, Southern University of Science and Technology, Shenzhen 518055, China.
Siyu QiaoSystems Biology Lab for Metabolic Reprogramming, Department of Human Genetics and Cell Biology, School of Medicine, Southern University of Science and Technology, Shenzhen 518055, China.
Bocheng ShiSystems Biology Lab for Metabolic Reprogramming, Department of Human Genetics and Cell Biology, School of Medicine, Southern University of Science and Technology, Shenzhen 518055, China.ORCID 0009-0008-4362-2615 Gangyi XiaoCollege of Computer Science and Technology, Jilin University, Changchun 130012, China.
Ying XuSystems Biology Lab for Metabolic Reprogramming, Department of Human Genetics and Cell Biology, School of Medicine, Southern University of Science and Technology, Shenzhen 518055, China.
Funding
The Development Project of Jilin Province of China No. 20220508125RCthe Key University Laboratory of Metabolism and Health of Guangdong, Southern University of Science and Technology, Shenzhen 518055, China 2022KSYS007The National Natural Science Foundation of China T2350010the National Natural Science Fund of China Research Fund for International Scientists W2431059
6 · The paper itselfAbstract
This study introduces a novel framework that simultaneously addresses the challenges of performance accuracy and result interpretability in transcriptomic-data-based classification.
Indexed as
Computational BiologyGene Expression ProfilingTranscriptomeAlgorithmsHumansMetabolic Networks and Pathwaysfeature gene selectioninterpretabilitymachine learningmodel selection
Identifiers
PMID40149449
PMCPMC11942234
What OpenQuestion holds
Textmetadata
LicenceCC BY
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