Image 7_Comprehensive integration of diagnostic biomarker analysis and immune cell infiltration features in sepsis via machine learning and bioinformatics techniques.tif by Liuqing Yang (128705)

“…Astragalus, Salvia, and Safflower are the primary elements of Xuebijing, commonly used in the clinical treatment of sepsis. …”

Image 1_Comprehensive integration of diagnostic biomarker analysis and immune cell infiltration features in sepsis via machine learning and bioinformatics techniques.tif by Liuqing Yang (128705)

“…Astragalus, Salvia, and Safflower are the primary elements of Xuebijing, commonly used in the clinical treatment of sepsis. …”

Image 2_Comprehensive integration of diagnostic biomarker analysis and immune cell infiltration features in sepsis via machine learning and bioinformatics techniques.tif by Liuqing Yang (128705)

“…Astragalus, Salvia, and Safflower are the primary elements of Xuebijing, commonly used in the clinical treatment of sepsis. …”

Data Sheet 1_Comprehensive integration of diagnostic biomarker analysis and immune cell infiltration features in sepsis via machine learning and bioinformatics techniques.zip by Liuqing Yang (128705)

“…Astragalus, Salvia, and Safflower are the primary elements of Xuebijing, commonly used in the clinical treatment of sepsis. …”

Table 1_Comprehensive integration of diagnostic biomarker analysis and immune cell infiltration features in sepsis via machine learning and bioinformatics techniques.docx by Liuqing Yang (128705)

“…Astragalus, Salvia, and Safflower are the primary elements of Xuebijing, commonly used in the clinical treatment of sepsis. …”

Image 5_Comprehensive integration of diagnostic biomarker analysis and immune cell infiltration features in sepsis via machine learning and bioinformatics techniques.tif by Liuqing Yang (128705)

“…Astragalus, Salvia, and Safflower are the primary elements of Xuebijing, commonly used in the clinical treatment of sepsis. …”

Image 1_Role of inflammation-related genes as prognostic biomarkers and mechanistic implications in idiopathic pulmonary fibrosis.tif by Bing Bai (331979)

“…Unsupervised clustering algorithms were used to classify IPF samples, followed by weighted gene co-expression network analysis (WGCNA) to identify highly correlated genes. …”

Table 1_Role of inflammation-related genes as prognostic biomarkers and mechanistic implications in idiopathic pulmonary fibrosis.xlsx by Bing Bai (331979)

“…Unsupervised clustering algorithms were used to classify IPF samples, followed by weighted gene co-expression network analysis (WGCNA) to identify highly correlated genes. …”

Table 1_A novel muscle network approach for objective assessment and profiling of bulbar involvement in ALS.docx by Panying Rong (2697181)

“…To evaluate the utility of the muscle network as a bulbar measurement tool, a heterogenous ALS cohort, consisting of eight individuals with overt clinical bulbar symptoms and seven without, along with 10 neurologically healthy controls, was employed to train and validate statistical and machine learning algorithms to assess the disease effects on the network features and the relation of the network performance to the current clinical diagnostic standard and behavioral patterns of bulbar involvement.…”

Image2_Identification of novel biomarkers, shared molecular signatures and immune cell infiltration in heart and kidney failure by transcriptomics.tif by Qingqing Long (845156)

“…A protein-protein interaction (PPI) network was constructed, and machine learning algorithms, including Random Forest (RF), Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and Least Absolute Shrinkage and Selection Operator (LASSO), were used to identify key signature genes. …”

Image1_Identification of novel biomarkers, shared molecular signatures and immune cell infiltration in heart and kidney failure by transcriptomics.tif by Qingqing Long (845156)

“…A protein-protein interaction (PPI) network was constructed, and machine learning algorithms, including Random Forest (RF), Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and Least Absolute Shrinkage and Selection Operator (LASSO), were used to identify key signature genes. …”

DataSheet1_Identification of novel biomarkers, shared molecular signatures and immune cell infiltration in heart and kidney failure by transcriptomics.docx by Qingqing Long (845156)

“…A protein-protein interaction (PPI) network was constructed, and machine learning algorithms, including Random Forest (RF), Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and Least Absolute Shrinkage and Selection Operator (LASSO), were used to identify key signature genes. …”

Data Sheet 1_Inflammation-related biomarkers and berberine therapy in post-stroke depression: evidence from bioinformatics, machine learning, and experimental validation.docx by Wei Liu (20030)

“…Three machine learning algorithms—random forest, support vector machine-recursive feature elimination (SVM-RFE), and least absolute shrinkage and selection operator (LASSO)—were applied to screen inflammation-related hub genes. …”

Etodolac utility in osteoarthritis: drug delivery challenges, topical nanotherapeutic strategies and potential synergies by Pavani Gaddala (19761334)

“…</p> <p>Biomechanical modeling integrated with artificial intelligence algorithms is advantageous in predicting onset and progression of knee osteoarthritis.…”

Climate anomalies due to Cerrado native vegetation loss by Argemiro Leite-Filho (10283801)

“…</li></ul><p dir="ltr"><b>Code/software</b></p><p dir="ltr">To analyze the CSV files in your dataset, you can use various software options, such as R and Microsoft Excel. …”