WGCNA and machine learning algorithms. by Shushu Wang (6870191)

Key gene identification based on two machine learning algorithms. by Lijun Guo (605276)

Integration of three machine learning algorithms to characterize lipid metabolism-associated hub genes in sepsis and atrial fibrillation. by Changze Ou (22806374)

Construction of the PRG score index using integrated machine learning algorithms. by Xiangxin Wu (22320058)

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Left: the algorithm’s best estimate of where to put breakpoints for each number of breakpoints considered. by Zhengtian Li (5076704)

Model construction and validation (A) Construction of the model based on multiple machine learning algorithms. by Chengting Wu (21485612)

Machine learning analysis of 10 hub genes related to SCI was performed using eight algorithms applied to the dataset. by Tianbao Feng (21722233)

Functional analysis of anoikis-related genes. by Yufeng He (5673119)

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(a) The volcano plot of GSE89408, where the red scatter represents the upregulated genes and the blue scatter represents the downregulated genes. (b) The heatmap of GSE89408. by Devi Soorya Narayana Sasikumar (22331743)

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Cell-type marker genes and functional enrichment analysis. by Hailin Li (453832)

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(a-b) The LASSO algorithm was used for hyperparameter tuning and identification of the important predictive features. by Devi Soorya Narayana Sasikumar (22331743)

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Performance of ENQUIRE’s gene normalization algorithm. by Luca Musella (19986312)

Functional enrichment analyses of Co-genes. by Qingcong Zheng (11169094)

Pipeline of the interactor algorithm. by Jose Cleydson F. Silva (8685594)

Functional enrichment and correlation analysis of hub DE-SRGs. by Xihao Shen (20347942)

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Analyses of Immune microenvironment and functions between gene clusters by Jiangli Zhao (2308330)

The gene set related to the “Cytotoxicity” and “Progenitor exhaustion” function of T cells. by Yanfeng Xue (465345)

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Multimodal reference functions. by Ruiyu Zhan (21602031)

“…Notably, the F1-scores, which balance precision and recall, were 0.69 for miRNA expression, 0.65 for gene expression, and 0.62 for DNA methylation. This research not only advances the application of machine learning in medical prognosis but also offers crucial guidance for clinicians in developing more precise and reliable prognostic tools for cancer patients. …”

The convergence curves of the test functions. by Ruiyu Zhan (21602031)

“…Notably, the F1-scores, which balance precision and recall, were 0.69 for miRNA expression, 0.65 for gene expression, and 0.62 for DNA methylation. This research not only advances the application of machine learning in medical prognosis but also offers crucial guidance for clinicians in developing more precise and reliable prognostic tools for cancer patients. …”

Single-peaked reference functions. by Ruiyu Zhan (21602031)

“…Notably, the F1-scores, which balance precision and recall, were 0.69 for miRNA expression, 0.65 for gene expression, and 0.62 for DNA methylation. This research not only advances the application of machine learning in medical prognosis but also offers crucial guidance for clinicians in developing more precise and reliable prognostic tools for cancer patients. …”