Table 1_Key gene screening and diagnostic model establishment for acute type a aortic dissection.xlsx by Yue Pan (267516)

“…</p>Methods<p>Transcriptome datasets from the Gene Expression Omnibus (GEO) database were analyzed using differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms (SVM, Random Forest, LASSO regression). Functional enrichment and immunoinfiltration analyses were performed to explore biological pathways and immune cell interactions. …”

Data Sheet 4_Key gene screening and diagnostic model establishment for acute type a aortic dissection.csv by Yue Pan (267516)

“…</p>Methods<p>Transcriptome datasets from the Gene Expression Omnibus (GEO) database were analyzed using differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms (SVM, Random Forest, LASSO regression). Functional enrichment and immunoinfiltration analyses were performed to explore biological pathways and immune cell interactions. …”

Data Sheet 5_Key gene screening and diagnostic model establishment for acute type a aortic dissection.csv by Yue Pan (267516)

“…</p>Methods<p>Transcriptome datasets from the Gene Expression Omnibus (GEO) database were analyzed using differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms (SVM, Random Forest, LASSO regression). Functional enrichment and immunoinfiltration analyses were performed to explore biological pathways and immune cell interactions. …”

Data Sheet 1_Key gene screening and diagnostic model establishment for acute type a aortic dissection.csv by Yue Pan (267516)

“…</p>Methods<p>Transcriptome datasets from the Gene Expression Omnibus (GEO) database were analyzed using differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms (SVM, Random Forest, LASSO regression). Functional enrichment and immunoinfiltration analyses were performed to explore biological pathways and immune cell interactions. …”

Video 5_Biologically informed algorithms for modeling cattle response to auditory cues and driving simulations in virtual fencing systems.mp4 by Hannah James (6551678)

Video 8_Biologically informed algorithms for modeling cattle response to auditory cues and driving simulations in virtual fencing systems.mp4 by Hannah James (6551678)

Video 6_Biologically informed algorithms for modeling cattle response to auditory cues and driving simulations in virtual fencing systems.mp4 by Hannah James (6551678)

Video 1_Biologically informed algorithms for modeling cattle response to auditory cues and driving simulations in virtual fencing systems.mp4 by Hannah James (6551678)

Supplementary file 1_Biologically informed algorithms for modeling cattle response to auditory cues and driving simulations in virtual fencing systems.docx by Hannah James (6551678)

Video 9_Biologically informed algorithms for modeling cattle response to auditory cues and driving simulations in virtual fencing systems.mp4 by Hannah James (6551678)

Video 3_Biologically informed algorithms for modeling cattle response to auditory cues and driving simulations in virtual fencing systems.mp4 by Hannah James (6551678)

Video 7_Biologically informed algorithms for modeling cattle response to auditory cues and driving simulations in virtual fencing systems.mp4 by Hannah James (6551678)

Video 4_Biologically informed algorithms for modeling cattle response to auditory cues and driving simulations in virtual fencing systems.mp4 by Hannah James (6551678)

Video 2_Biologically informed algorithms for modeling cattle response to auditory cues and driving simulations in virtual fencing systems.mp4 by Hannah James (6551678)

Image 1_MS4A7 based metabolic gene signature as a prognostic predictor in lung adenocarcinoma.jpeg by Yan Jiang (12139)

“…</p>Methods<p>A prognostic signature for LUAD was developed using the LASSO-Cox regression algorithm with RNA-seq data from 500 LUAD patients in The Cancer Genome Atlas database. …”

Image 3_MS4A7 based metabolic gene signature as a prognostic predictor in lung adenocarcinoma.jpeg by Yan Jiang (12139)

“…</p>Methods<p>A prognostic signature for LUAD was developed using the LASSO-Cox regression algorithm with RNA-seq data from 500 LUAD patients in The Cancer Genome Atlas database. …”

Image 10_MS4A7 based metabolic gene signature as a prognostic predictor in lung adenocarcinoma.jpeg by Yan Jiang (12139)

“…</p>Methods<p>A prognostic signature for LUAD was developed using the LASSO-Cox regression algorithm with RNA-seq data from 500 LUAD patients in The Cancer Genome Atlas database. …”

Image 4_MS4A7 based metabolic gene signature as a prognostic predictor in lung adenocarcinoma.jpeg by Yan Jiang (12139)

“…</p>Methods<p>A prognostic signature for LUAD was developed using the LASSO-Cox regression algorithm with RNA-seq data from 500 LUAD patients in The Cancer Genome Atlas database. …”

Image 8_MS4A7 based metabolic gene signature as a prognostic predictor in lung adenocarcinoma.jpeg by Yan Jiang (12139)

“…</p>Methods<p>A prognostic signature for LUAD was developed using the LASSO-Cox regression algorithm with RNA-seq data from 500 LUAD patients in The Cancer Genome Atlas database. …”

Image 2_MS4A7 based metabolic gene signature as a prognostic predictor in lung adenocarcinoma.jpeg by Yan Jiang (12139)

“…</p>Methods<p>A prognostic signature for LUAD was developed using the LASSO-Cox regression algorithm with RNA-seq data from 500 LUAD patients in The Cancer Genome Atlas database. …”