Image 1_Characterizing clinical risk profiles of major complications in type 2 diabetes mellitus using deep learning algorithms.jpeg by Haochen Liu (487914)

“…</p>Materials and methods<p>Data from 1,448 T2DM patients at Xi’an No.9 Hospital were used. …”

Table 4_Characterizing clinical risk profiles of major complications in type 2 diabetes mellitus using deep learning algorithms.xlsx by Haochen Liu (487914)

“…</p>Materials and methods<p>Data from 1,448 T2DM patients at Xi’an No.9 Hospital were used. …”

Table 5_Characterizing clinical risk profiles of major complications in type 2 diabetes mellitus using deep learning algorithms.xlsx by Haochen Liu (487914)

“…</p>Materials and methods<p>Data from 1,448 T2DM patients at Xi’an No.9 Hospital were used. …”

Image 3_Characterizing clinical risk profiles of major complications in type 2 diabetes mellitus using deep learning algorithms.jpeg by Haochen Liu (487914)

“…</p>Materials and methods<p>Data from 1,448 T2DM patients at Xi’an No.9 Hospital were used. …”

Table 3_Characterizing clinical risk profiles of major complications in type 2 diabetes mellitus using deep learning algorithms.xlsx by Haochen Liu (487914)

“…</p>Materials and methods<p>Data from 1,448 T2DM patients at Xi’an No.9 Hospital were used. …”

Table 1_Characterizing clinical risk profiles of major complications in type 2 diabetes mellitus using deep learning algorithms.xlsx by Haochen Liu (487914)

“…</p>Materials and methods<p>Data from 1,448 T2DM patients at Xi’an No.9 Hospital were used. …”

Table 5_Feature genes identification and immune infiltration assessment in abdominal aortic aneurysm using WGCNA and machine learning algorithms.xls by Ming Xie (420493)

“…We then utilized weighted gene co-expression network analysis (WGCNA) along with 3 machine learning techniques: least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and random forest, to identify feature genes for AAA. Moreover, data were validated using the receiver operating characteristic (ROC) curve, with feature genes defined as those having an area under the curve above 85% and a p-value below 0.05. …”

Table 8_Feature genes identification and immune infiltration assessment in abdominal aortic aneurysm using WGCNA and machine learning algorithms.xls by Ming Xie (420493)

“…We then utilized weighted gene co-expression network analysis (WGCNA) along with 3 machine learning techniques: least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and random forest, to identify feature genes for AAA. Moreover, data were validated using the receiver operating characteristic (ROC) curve, with feature genes defined as those having an area under the curve above 85% and a p-value below 0.05. …”

Table 7_Feature genes identification and immune infiltration assessment in abdominal aortic aneurysm using WGCNA and machine learning algorithms.xls by Ming Xie (420493)

“…We then utilized weighted gene co-expression network analysis (WGCNA) along with 3 machine learning techniques: least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and random forest, to identify feature genes for AAA. Moreover, data were validated using the receiver operating characteristic (ROC) curve, with feature genes defined as those having an area under the curve above 85% and a p-value below 0.05. …”

Table 4_Feature genes identification and immune infiltration assessment in abdominal aortic aneurysm using WGCNA and machine learning algorithms.xls by Ming Xie (420493)

“…We then utilized weighted gene co-expression network analysis (WGCNA) along with 3 machine learning techniques: least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and random forest, to identify feature genes for AAA. Moreover, data were validated using the receiver operating characteristic (ROC) curve, with feature genes defined as those having an area under the curve above 85% and a p-value below 0.05. …”

Table 6_Feature genes identification and immune infiltration assessment in abdominal aortic aneurysm using WGCNA and machine learning algorithms.xls by Ming Xie (420493)

“…We then utilized weighted gene co-expression network analysis (WGCNA) along with 3 machine learning techniques: least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and random forest, to identify feature genes for AAA. Moreover, data were validated using the receiver operating characteristic (ROC) curve, with feature genes defined as those having an area under the curve above 85% and a p-value below 0.05. …”

Table 3_Feature genes identification and immune infiltration assessment in abdominal aortic aneurysm using WGCNA and machine learning algorithms.xls by Ming Xie (420493)

“…We then utilized weighted gene co-expression network analysis (WGCNA) along with 3 machine learning techniques: least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and random forest, to identify feature genes for AAA. Moreover, data were validated using the receiver operating characteristic (ROC) curve, with feature genes defined as those having an area under the curve above 85% and a p-value below 0.05. …”

Table 2_Feature genes identification and immune infiltration assessment in abdominal aortic aneurysm using WGCNA and machine learning algorithms.xls by Ming Xie (420493)

“…We then utilized weighted gene co-expression network analysis (WGCNA) along with 3 machine learning techniques: least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and random forest, to identify feature genes for AAA. Moreover, data were validated using the receiver operating characteristic (ROC) curve, with feature genes defined as those having an area under the curve above 85% and a p-value below 0.05. …”

Table 1_Feature genes identification and immune infiltration assessment in abdominal aortic aneurysm using WGCNA and machine learning algorithms.xls by Ming Xie (420493)

“…We then utilized weighted gene co-expression network analysis (WGCNA) along with 3 machine learning techniques: least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and random forest, to identify feature genes for AAA. Moreover, data were validated using the receiver operating characteristic (ROC) curve, with feature genes defined as those having an area under the curve above 85% and a p-value below 0.05. …”

China’s annual forest age dataset at a 30-m spatial resolution from 1986 to 2022 by Rong Shang (17306089)

S7 Data - Landslide hazard early warning method for rock slopes using a hybrid LSTM-SARIMA data-driven model by Yongxin Dai (13796385)

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S6 Data - Landslide hazard early warning method for rock slopes using a hybrid LSTM-SARIMA data-driven model by Yongxin Dai (13796385)

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S13 Data - Landslide hazard early warning method for rock slopes using a hybrid LSTM-SARIMA data-driven model by Yongxin Dai (13796385)

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S7 Data - Landslide hazard early warning method for rock slopes using a hybrid LSTM-SARIMA data-driven model by Yongxin Dai (13796385)

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S11 Data - Landslide hazard early warning method for rock slopes using a hybrid LSTM-SARIMA data-driven model by Yongxin Dai (13796385)

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