Table_1_High-order brain network feature extraction and classification method of first-episode schizophrenia: an EEG study.csv حسب Yanxia Kang (9949844)

"…The univariate feature selection algorithm achieved a classification accuracy of 94.6% with a combination of attributes meeting the criterion of AC ≥ 0.6.…"

Table 2_Exploration of the shared gene signatures and molecular mechanisms between cardioembolic stroke and ischemic stroke.xlsx حسب Xuan Wang (55634)

"…</p>Results<p>There were 125 shared up-regulated genes and 2 shared down-regulated between CS and IS, which were mainly involved in immune inflammatory response-related biological functions. The Maximum Clique Centrality algorithm identified 25 core shared genes in the PPI network constructed using the shared genes. …"

Table 3_Exploration of the shared gene signatures and molecular mechanisms between cardioembolic stroke and ischemic stroke.xlsx حسب Xuan Wang (55634)

"…</p>Results<p>There were 125 shared up-regulated genes and 2 shared down-regulated between CS and IS, which were mainly involved in immune inflammatory response-related biological functions. The Maximum Clique Centrality algorithm identified 25 core shared genes in the PPI network constructed using the shared genes. …"

Table 1_Exploration of the shared gene signatures and molecular mechanisms between cardioembolic stroke and ischemic stroke.xlsx حسب Xuan Wang (55634)

"…</p>Results<p>There were 125 shared up-regulated genes and 2 shared down-regulated between CS and IS, which were mainly involved in immune inflammatory response-related biological functions. The Maximum Clique Centrality algorithm identified 25 core shared genes in the PPI network constructed using the shared genes. …"

Supplementary file 1_SLC11A1 protein as a key regulator of iron metabolism, ferroptosis mediator, and putative therapeutic target in nonalcoholic fatty liver disease: an integrated... حسب Yang Wang (5921)

"…Key regulatory proteins—ERN1, SLC11A1, MYC, TLR7, and PPARGC1A—were screened using weighted gene co-expression network analysis (WGCNA) and a machine learning algorithm (LASSO). Their correlations with immune microenvironment features were also evaluated. …"

Table 12_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx حسب Ting Wu (106368)

"…</p>Results<p>Through machine learning algorithms, RPL14, SMARCD3, and TCN1 were identified as candidate biomarkers. …"

Table 9_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx حسب Ting Wu (106368)

"…</p>Results<p>Through machine learning algorithms, RPL14, SMARCD3, and TCN1 were identified as candidate biomarkers. …"

Table 8_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx حسب Ting Wu (106368)

"…</p>Results<p>Through machine learning algorithms, RPL14, SMARCD3, and TCN1 were identified as candidate biomarkers. …"

Table 1_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx حسب Ting Wu (106368)

"…</p>Results<p>Through machine learning algorithms, RPL14, SMARCD3, and TCN1 were identified as candidate biomarkers. …"

Table 2_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx حسب Ting Wu (106368)

"…</p>Results<p>Through machine learning algorithms, RPL14, SMARCD3, and TCN1 were identified as candidate biomarkers. …"

Table 3_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx حسب Ting Wu (106368)

"…</p>Results<p>Through machine learning algorithms, RPL14, SMARCD3, and TCN1 were identified as candidate biomarkers. …"

Table 5_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.doc حسب Ting Wu (106368)

"…</p>Results<p>Through machine learning algorithms, RPL14, SMARCD3, and TCN1 were identified as candidate biomarkers. …"

Table 13_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.doc حسب Ting Wu (106368)

"…</p>Results<p>Through machine learning algorithms, RPL14, SMARCD3, and TCN1 were identified as candidate biomarkers. …"

Table 6_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx حسب Ting Wu (106368)

"…</p>Results<p>Through machine learning algorithms, RPL14, SMARCD3, and TCN1 were identified as candidate biomarkers. …"

Table 11_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx حسب Ting Wu (106368)

"…</p>Results<p>Through machine learning algorithms, RPL14, SMARCD3, and TCN1 were identified as candidate biomarkers. …"

Table 7_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx حسب Ting Wu (106368)

"…</p>Results<p>Through machine learning algorithms, RPL14, SMARCD3, and TCN1 were identified as candidate biomarkers. …"

Table 10_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx حسب Ting Wu (106368)

"…</p>Results<p>Through machine learning algorithms, RPL14, SMARCD3, and TCN1 were identified as candidate biomarkers. …"

Table 4_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx حسب Ting Wu (106368)

"…</p>Results<p>Through machine learning algorithms, RPL14, SMARCD3, and TCN1 were identified as candidate biomarkers. …"

Image 2_Intra-tumor heterogeneity-resistant gene signature predicts prognosis and immune infiltration in breast cancer.jpeg حسب Haixing Shen (22315486)

"…Genes resistant to sampling bias were identified by evaluating inter-patient heterogeneity (IPH) and ITH. A machine learning framework incorporating ten algorithms was used to construct a prognostic signature.The expression levels and oncogenic function of the prognostic genes were validated through RT-qPCR and in vitro experiments.…"

Image 1_Intra-tumor heterogeneity-resistant gene signature predicts prognosis and immune infiltration in breast cancer.jpeg حسب Haixing Shen (22315486)

"…Genes resistant to sampling bias were identified by evaluating inter-patient heterogeneity (IPH) and ITH. A machine learning framework incorporating ten algorithms was used to construct a prognostic signature.The expression levels and oncogenic function of the prognostic genes were validated through RT-qPCR and in vitro experiments.…"