Table 1 - by Mahsa Alem (22446685)

“…The epitope thresholds for classification as strong, mild, and weak are less than 2% for strong, between 2% and 5% for mild, and between 5% and 10% for weak. …”

Prediction probabilities for discerning . by Saul Fuster (22339894)

“…To address this challenge, we propose a novel three-part framework comprising of a convolutional network based tissue segmentation algorithm for region of interest delineation, a contrastive learning module for feature extraction, and a nested multiple instance learning classification module. …”

Data Sheet 2_Identification of progression-related genes and construction of prognostic model for chronic kidney disease by machine learning.xlsx by Bingkun Zhou (12267614)

“…However, there is currently a lack of CKD prognostic prediction models based on transcriptomics and machine learning.</p>Methods<p>Utilizing weighted correlation network analysis (WGCNA) and random forest algorithms in GSE137570, three core gene sets of different sizes were constructed, which were externally validated in GSE66494 and GSE180394, and evaluated for their predictive performance in GSE45980 by receiver operating characteristic (ROC) curves. …”

Data Sheet 1_Identification of progression-related genes and construction of prognostic model for chronic kidney disease by machine learning.docx by Bingkun Zhou (12267614)

“…However, there is currently a lack of CKD prognostic prediction models based on transcriptomics and machine learning.</p>Methods<p>Utilizing weighted correlation network analysis (WGCNA) and random forest algorithms in GSE137570, three core gene sets of different sizes were constructed, which were externally validated in GSE66494 and GSE180394, and evaluated for their predictive performance in GSE45980 by receiver operating characteristic (ROC) curves. …”

Data Sheet 1_Cerebral gray matter volume identifies healthy older drivers with a critical decline in driving safety performance using actual vehicles on a closed-circuit course.pdf by Handityo Aulia Putra (21430112)

“…Regional GM volumes were quantified using voxel-based morphometry of MRI scans. Feature selection and classification were performed using the Random Forest machine learning algorithm, optimized to identify the most predictive GM regions.…”

Table 1_Exploring predictors of insomnia severity in shift workers using machine learning model.docx by Hyewon Yeo (14255972)

“…</p>Discussion<p>This ML algorithm provides an effective method for identifying key factors that predict insomnia severity in shift workers. …”

Machine learning-enhanced monitoring of global copper mining areas by Houxuan Li (20960950)

“…Data collection involved preprocessing Sentinel-2 satellite imagery via the Google Earth Engine (GEE) platform, applying cloud-free median composites, and training a Random Forest classification algorithm using manually collected sample points for accurate delineation of mining features. …”

Image 1_Integrated analysis of single-cell and bulk transcriptomic data reveals altered cellular composition and predictive cell types in ectopic endometriosis.jpeg by Meihong Chen (8434173)

“…A comprehensive investigation of the cellular classification and composition of endometriosis is essential for studying its diagnosis and treatment.…”

Image 2_Integrated analysis of single-cell and bulk transcriptomic data reveals altered cellular composition and predictive cell types in ectopic endometriosis.jpeg by Meihong Chen (8434173)

“…A comprehensive investigation of the cellular classification and composition of endometriosis is essential for studying its diagnosis and treatment.…”

Unmixing marsh vegetation species across multiple sensors and spatial scales by Zhicheng Yang (216080)

“…Specifically, the FA values of each species within the pixels from airborne, WorldView-2 (WV2), and Sentinel-2 (SL2) data, with pixel sizes of 0.15 m, 0.5 m, and 10 m, respectively, were estimated by using a Rescaled Random Forest Regression (RRFR) algorithm. Our results suggest that Random Forest Classification can accurately classify marsh vegetation, with extremely high levels of accuracy when applied to UAV data. …”

<b>From street view imagery to the countryside: large-scale perception of rural China using deep learning</b> by Yao Yao (7903457)

“…The label field is a binary classification result: 0 means the left image is better than the right image, and 1 means the opposite.…”

Image 5_Integrated analysis of single-cell and bulk transcriptomic data reveals altered cellular composition and predictive cell types in ectopic endometriosis.jpeg by Meihong Chen (8434173)

“…A comprehensive investigation of the cellular classification and composition of endometriosis is essential for studying its diagnosis and treatment.…”

Table 1_Integrated analysis of single-cell and bulk transcriptomic data reveals altered cellular composition and predictive cell types in ectopic endometriosis.docx by Meihong Chen (8434173)

“…A comprehensive investigation of the cellular classification and composition of endometriosis is essential for studying its diagnosis and treatment.…”

Image 4_Integrated analysis of single-cell and bulk transcriptomic data reveals altered cellular composition and predictive cell types in ectopic endometriosis.jpeg by Meihong Chen (8434173)

“…A comprehensive investigation of the cellular classification and composition of endometriosis is essential for studying its diagnosis and treatment.…”

Image 3_Integrated analysis of single-cell and bulk transcriptomic data reveals altered cellular composition and predictive cell types in ectopic endometriosis.jpeg by Meihong Chen (8434173)

“…A comprehensive investigation of the cellular classification and composition of endometriosis is essential for studying its diagnosis and treatment.…”

Table 1_An immunotherapy guide constructed by cGAS-STING signature for breast cancer and the biofunction validation of the pivotal gene HOXC13 via in vitro experiments.docx by Zhenling Dai (22011134)

“…Employing machine learning, our novel classification model based on the 11-gene signature effectively differentiated between high-response and low-response groups in 16 out of 18 independent breast cancer cohorts from the GEO database. …”

Image 2_An immunotherapy guide constructed by cGAS-STING signature for breast cancer and the biofunction validation of the pivotal gene HOXC13 via in vitro experiments.tif by Zhenling Dai (22011134)

“…Employing machine learning, our novel classification model based on the 11-gene signature effectively differentiated between high-response and low-response groups in 16 out of 18 independent breast cancer cohorts from the GEO database. …”

Image 1_An immunotherapy guide constructed by cGAS-STING signature for breast cancer and the biofunction validation of the pivotal gene HOXC13 via in vitro experiments.tif by Zhenling Dai (22011134)

“…Employing machine learning, our novel classification model based on the 11-gene signature effectively differentiated between high-response and low-response groups in 16 out of 18 independent breast cancer cohorts from the GEO database. …”

Data Sheet 2_Multi-omics analysis untangles the crosstalk between intratumor microbiome, lactic acid metabolism and immune status in lung squamous cell carcinoma.zip by Xun Qiu (3223218)

“…Multiple machine learning algorithms were used to generate the LUSC classification. …”

Data Sheet 1_Multi-omics analysis untangles the crosstalk between intratumor microbiome, lactic acid metabolism and immune status in lung squamous cell carcinoma.docx by Xun Qiu (3223218)

“…Multiple machine learning algorithms were used to generate the LUSC classification. …”