Image 2_Integrating scRNA-seq and machine learning identifies MNAT1 as a therapeutic target in OSCC.tif by Han Gao (486886)

“…</p>Method<p>T cell-Related Ubiquitination genes were identified based on scRNA-seq analysis, and key genes were selected using WGCNA and LASSO algorithms to construct a prognostic model. …”

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)

“…A comprehensive machine learning framework integrating 101 algorithms was applied to construct a glycosyltransferase-based prognostic signature. …”

Image 1_Integrating scRNA-seq and machine learning identifies MNAT1 as a therapeutic target in OSCC.tif by Han Gao (486886)

“…</p>Method<p>T cell-Related Ubiquitination genes were identified based on scRNA-seq analysis, and key genes were selected using WGCNA and LASSO algorithms to construct a prognostic model. …”

Test results of 16 methods in edge detection. by Yuliang Chi (21276505)

“…These problems can lead to difficulties in underwater information extraction and affect the development of underwater research, so we propose a multi-task hybrid fusion method (MHF) for underwater image enhancement based on biological vision. …”

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)

“…A comprehensive machine learning framework integrating 101 algorithms was applied to construct a glycosyltransferase-based prognostic signature. …”

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

“…A comprehensive machine learning framework integrating 101 algorithms was applied to construct a glycosyltransferase-based prognostic signature. …”

Table 1_Integrating scRNA-seq and machine learning identifies MNAT1 as a therapeutic target in OSCC.docx by Han Gao (486886)

“…</p>Method<p>T cell-Related Ubiquitination genes were identified based on scRNA-seq analysis, and key genes were selected using WGCNA and LASSO algorithms to construct a prognostic model. …”

Image 4_Integrating scRNA-seq and machine learning identifies MNAT1 as a therapeutic target in OSCC.tif by Han Gao (486886)

“…</p>Method<p>T cell-Related Ubiquitination genes were identified based on scRNA-seq analysis, and key genes were selected using WGCNA and LASSO algorithms to construct a prognostic model. …”

Test results of 16 methods in target detection. by Yuliang Chi (21276505)

“…These problems can lead to difficulties in underwater information extraction and affect the development of underwater research, so we propose a multi-task hybrid fusion method (MHF) for underwater image enhancement based on biological vision. …”

Image 2_Construction of a glycosylation-related prognostic signature for predicting prognosis, tumor microenvironment, and immune response in soft tissue sarcoma.jpeg by Ningning Yuan (4231858)

“…A comprehensive machine learning framework integrating 101 algorithms was applied to construct a glycosyltransferase-based prognostic signature. …”

Automatic data reduction for the typical astronomer by Bradford Holden (21789524)

“…PypeIt has been developed by a small team of astronomers with two leading philosophies: (1) build instrument-agnostic code to serve nearly any spectrograph; (2) implement algorithms that achieve Poisson-level sky-subtraction with minimal systematics to yield precisely calibrated spectra with a meaningful noise model. …”

Data Sheet 1_Segmentation and modeling of large-scale microvascular networks: a survey.pdf by Helya Goharbavang (22535498)

“…We select a series of algorithms based on popularity and availability and provide a thorough quantitative analysis of their performance on datasets acquired using light sheet fluorescence microscopy (LSFM), knife-edge scanning microscopy (KESM), and X-ray microtomography (µ-CT).…”

Data Sheet 1_Identification and experimental validation of biomarkers associated with mitochondrial and programmed cell death in major depressive disorder.pdf by Shengjie Xiong (21216725)

“…Machine learning and gene expression validation were employed, followed by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) for further validation. A nomogram was developed to predict MDD probability based on biomarkers. …”

Table 10_Identification and experimental validation of biomarkers associated with mitochondrial and programmed cell death in major depressive disorder.xlsx by Shengjie Xiong (21216725)

“…Machine learning and gene expression validation were employed, followed by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) for further validation. A nomogram was developed to predict MDD probability based on biomarkers. …”

Table 4_Identification and experimental validation of biomarkers associated with mitochondrial and programmed cell death in major depressive disorder.xlsx by Shengjie Xiong (21216725)

“…Machine learning and gene expression validation were employed, followed by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) for further validation. A nomogram was developed to predict MDD probability based on biomarkers. …”

Table 6_Identification and experimental validation of biomarkers associated with mitochondrial and programmed cell death in major depressive disorder.xlsx by Shengjie Xiong (21216725)

“…Machine learning and gene expression validation were employed, followed by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) for further validation. A nomogram was developed to predict MDD probability based on biomarkers. …”

Table 9_Identification and experimental validation of biomarkers associated with mitochondrial and programmed cell death in major depressive disorder.xlsx by Shengjie Xiong (21216725)

“…Machine learning and gene expression validation were employed, followed by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) for further validation. A nomogram was developed to predict MDD probability based on biomarkers. …”

Table 5_Identification and experimental validation of biomarkers associated with mitochondrial and programmed cell death in major depressive disorder.xlsx by Shengjie Xiong (21216725)

“…Machine learning and gene expression validation were employed, followed by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) for further validation. A nomogram was developed to predict MDD probability based on biomarkers. …”

Table 8_Identification and experimental validation of biomarkers associated with mitochondrial and programmed cell death in major depressive disorder.xlsx by Shengjie Xiong (21216725)

“…Machine learning and gene expression validation were employed, followed by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) for further validation. A nomogram was developed to predict MDD probability based on biomarkers. …”

Table 2_Identification and experimental validation of biomarkers associated with mitochondrial and programmed cell death in major depressive disorder.xlsx by Shengjie Xiong (21216725)

“…Machine learning and gene expression validation were employed, followed by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) for further validation. A nomogram was developed to predict MDD probability based on biomarkers. …”