Image 7_A machine learning-derived immune-related prognostic model identifies PLXNA3 as a functional risk gene in colorectal cancer.tif by Hanzhang Lyu (22163404)

“…Transcription factor (TF) and microRNA (miRNA) correlation analyses revealed potential upstream regulators of PLXNA3 linked to tumor stemness and immune suppression. Functional enrichment indicated its association with cell cycle, DNA damage repair, and interferon signaling pathways. …”

Image 6_A machine learning-derived immune-related prognostic model identifies PLXNA3 as a functional risk gene in colorectal cancer.tif by Hanzhang Lyu (22163404)

“…Transcription factor (TF) and microRNA (miRNA) correlation analyses revealed potential upstream regulators of PLXNA3 linked to tumor stemness and immune suppression. Functional enrichment indicated its association with cell cycle, DNA damage repair, and interferon signaling pathways. …”

Image 5_A machine learning-derived immune-related prognostic model identifies PLXNA3 as a functional risk gene in colorectal cancer.tif by Hanzhang Lyu (22163404)

“…Transcription factor (TF) and microRNA (miRNA) correlation analyses revealed potential upstream regulators of PLXNA3 linked to tumor stemness and immune suppression. Functional enrichment indicated its association with cell cycle, DNA damage repair, and interferon signaling pathways. …”

Image 4_A machine learning-derived immune-related prognostic model identifies PLXNA3 as a functional risk gene in colorectal cancer.tif by Hanzhang Lyu (22163404)

“…Transcription factor (TF) and microRNA (miRNA) correlation analyses revealed potential upstream regulators of PLXNA3 linked to tumor stemness and immune suppression. Functional enrichment indicated its association with cell cycle, DNA damage repair, and interferon signaling pathways. …”

Table 1_Kefir and healthy aging: revealing thematic gaps through AI-assisted screening and semantic evidence mapping.docx by Francesco Chiani (2661328)

“…To overcome this fragmentation, we applied an integrative approach that combines a cutting-edge AI-assisted algorithm for evidence screening with a Python-based semantic clustering pipeline. …”

Table 2_Kefir and healthy aging: revealing thematic gaps through AI-assisted screening and semantic evidence mapping.docx by Francesco Chiani (2661328)

“…To overcome this fragmentation, we applied an integrative approach that combines a cutting-edge AI-assisted algorithm for evidence screening with a Python-based semantic clustering pipeline. …”

Table 3_Kefir and healthy aging: revealing thematic gaps through AI-assisted screening and semantic evidence mapping.xlsx by Francesco Chiani (2661328)

“…To overcome this fragmentation, we applied an integrative approach that combines a cutting-edge AI-assisted algorithm for evidence screening with a Python-based semantic clustering pipeline. …”

Table 4_Kefir and healthy aging: revealing thematic gaps through AI-assisted screening and semantic evidence mapping.docx by Francesco Chiani (2661328)

“…To overcome this fragmentation, we applied an integrative approach that combines a cutting-edge AI-assisted algorithm for evidence screening with a Python-based semantic clustering pipeline. …”

RNA microarray analysis in knockdown of STAT1 in primary human hepatocyte HBV infection system and HBV-transfected HepG2 cells. by Takuto Nosaka (4484245)

RNA sequencing analysis and screening of anoikis-related signature genes. by Yanfeng Xue (465345)

Subjects: “…Cell Biology…”

The list of 434 anoikis-related genes (ARGs). by Yanfeng Xue (465345)

Subjects: “…Cell Biology…”

Analysis of differential enrichment and immune infiltration in high and low risk groups. by Yanfeng Xue (465345)

Subjects: “…Cell Biology…”

The univariate Cox regression analysis identified statistically significant anoikis-related DEGs (P < 0.05, hazard ratio≠1). by Yanfeng Xue (465345)

Subjects: “…Cell Biology…”

Construction and validation of a prognostic model for LUAD patients. by Yanfeng Xue (465345)

Subjects: “…Cell Biology…”

Workflow diagram for this study. by Yanfeng Xue (465345)

Subjects: “…Cell Biology…”

Table 6_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx by Anjan Kumar Pradhan (9386369)

“…Improved performances of the algorithms via feature selection from the raw gene features identified 235 unique genes as top candidate genes across all models for all stresses. …”

Table 7_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx by Anjan Kumar Pradhan (9386369)

“…Improved performances of the algorithms via feature selection from the raw gene features identified 235 unique genes as top candidate genes across all models for all stresses. …”

Table 3_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx by Anjan Kumar Pradhan (9386369)

“…Improved performances of the algorithms via feature selection from the raw gene features identified 235 unique genes as top candidate genes across all models for all stresses. …”

Table 2_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx by Anjan Kumar Pradhan (9386369)

“…Improved performances of the algorithms via feature selection from the raw gene features identified 235 unique genes as top candidate genes across all models for all stresses. …”

Table 1_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx by Anjan Kumar Pradhan (9386369)

“…Improved performances of the algorithms via feature selection from the raw gene features identified 235 unique genes as top candidate genes across all models for all stresses. …”