Table 6_MMPred: a tool to predict peptide mimicry events in MHC class II recognition.xlsx by Filippo Guerri (17017524)

“…<p>We present MMPred, a software tool that integrates epitope prediction and sequence alignment algorithms to streamline the computational analysis of molecular mimicry events in autoimmune diseases. …”

Table 2_MMPred: a tool to predict peptide mimicry events in MHC class II recognition.xlsx by Filippo Guerri (17017524)

“…<p>We present MMPred, a software tool that integrates epitope prediction and sequence alignment algorithms to streamline the computational analysis of molecular mimicry events in autoimmune diseases. …”

Table 4_MMPred: a tool to predict peptide mimicry events in MHC class II recognition.xlsx by Filippo Guerri (17017524)

“…<p>We present MMPred, a software tool that integrates epitope prediction and sequence alignment algorithms to streamline the computational analysis of molecular mimicry events in autoimmune diseases. …”

Table 7_MMPred: a tool to predict peptide mimicry events in MHC class II recognition.xlsx by Filippo Guerri (17017524)

“…<p>We present MMPred, a software tool that integrates epitope prediction and sequence alignment algorithms to streamline the computational analysis of molecular mimicry events in autoimmune diseases. …”

Table 1_MMPred: a tool to predict peptide mimicry events in MHC class II recognition.xlsx by Filippo Guerri (17017524)

“…<p>We present MMPred, a software tool that integrates epitope prediction and sequence alignment algorithms to streamline the computational analysis of molecular mimicry events in autoimmune diseases. …”

Table 8_MMPred: a tool to predict peptide mimicry events in MHC class II recognition.xlsx by Filippo Guerri (17017524)

“…<p>We present MMPred, a software tool that integrates epitope prediction and sequence alignment algorithms to streamline the computational analysis of molecular mimicry events in autoimmune diseases. …”

FAR1 as a ferroptosis-related biomarker and potential therapeutic target in acute kidney injury: integrated bioinformatics and experimental validation by Hao Duan (8386146)

“…Differentially expressed FRGs linked to AKI were identified through analytical methods, followed by an examination of their biological functions. Diagnostic biomarkers were then selected using LASSO, RFE, and RF algorithms. …”

Image 2_Development and validation of a machine learning-driven mitochondrial gene signature for the diagnosis of breast cancer.tif by Siyu Tong (12905585)

“…Using 113 machine learning algorithms and MitoCarta mitochondrial genetics data, we developed a mitochondrial gene-based diagnostic model. …”

Table 3_Development and validation of a machine learning-driven mitochondrial gene signature for the diagnosis of breast cancer.xlsx by Siyu Tong (12905585)

“…Using 113 machine learning algorithms and MitoCarta mitochondrial genetics data, we developed a mitochondrial gene-based diagnostic model. …”

Data Sheet 2_Development and validation of a machine learning-driven mitochondrial gene signature for the diagnosis of breast cancer.zip by Siyu Tong (12905585)

“…Using 113 machine learning algorithms and MitoCarta mitochondrial genetics data, we developed a mitochondrial gene-based diagnostic model. …”

Data Sheet 1_Development and validation of a machine learning-driven mitochondrial gene signature for the diagnosis of breast cancer.zip by Siyu Tong (12905585)

“…Using 113 machine learning algorithms and MitoCarta mitochondrial genetics data, we developed a mitochondrial gene-based diagnostic model. …”

Table 1_Development and validation of a machine learning-driven mitochondrial gene signature for the diagnosis of breast cancer.xlsx by Siyu Tong (12905585)

“…Using 113 machine learning algorithms and MitoCarta mitochondrial genetics data, we developed a mitochondrial gene-based diagnostic model. …”

Image 1_Development and validation of a machine learning-driven mitochondrial gene signature for the diagnosis of breast cancer.tif by Siyu Tong (12905585)

“…Using 113 machine learning algorithms and MitoCarta mitochondrial genetics data, we developed a mitochondrial gene-based diagnostic model. …”

Image 3_Development and validation of a machine learning-driven mitochondrial gene signature for the diagnosis of breast cancer.jpeg by Siyu Tong (12905585)

“…Using 113 machine learning algorithms and MitoCarta mitochondrial genetics data, we developed a mitochondrial gene-based diagnostic model. …”

Table 2_Development and validation of a machine learning-driven mitochondrial gene signature for the diagnosis of breast cancer.xlsx by Siyu Tong (12905585)

“…Using 113 machine learning algorithms and MitoCarta mitochondrial genetics data, we developed a mitochondrial gene-based diagnostic model. …”

Molecular modeling and SEC analysis of CAR and CD46 binding. by A. Manuel Liaci (20642831)

“…Residues that are only functionally conserved are colored yellow, residues that are not conserved are colored orange. …”

Table 1_Construction of a glycosylation-related prognostic signature for predicting prognosis, tumor microenvironment, and immune response in soft tissue sarcoma.docx by Ningning Yuan (4231858)

“…Background<p>Altered glycosylation, one of the most common post-translational protein modifications, plays a critical role in the initiation and progression of soft tissue sarcoma (STS). …”

Image 3_Construction of a glycosylation-related prognostic signature for predicting prognosis, tumor microenvironment, and immune response in soft tissue sarcoma.jpeg by Ningning Yuan (4231858)

“…Background<p>Altered glycosylation, one of the most common post-translational protein modifications, plays a critical role in the initiation and progression of soft tissue sarcoma (STS). …”

Network toxicology and machine learning reveal key molecular targets and pathways of mono-2-ethylhexyl phthalate-induced atherosclerosis by Qiu Sun (1607974)

“…Machine learning algorithms including LASSO regression, RF, and SVM were employed to identify key targets. …”

Table 2_Construction and validation of immune prognosis model for lung adenocarcinoma based on machine learning.docx by Jinyu Zheng (734257)

“…Immune infiltration was analyzed using TIMER and ssGSEA, with consensus clustering performed to explore immune subtypes. Protein expression and biological functions of hub genes were validated using the HPA database and GSEA.…”