Image 1_Integrative single-cell and spatial transcriptomics analysis reveals FLAD1 as a regulator of the immune microenvironment in hepatocellular carcinoma.tif حسب Peng Zhu (277243)

"…A modeling approach using 92 combinations of nine machine learning algorithms was applied, producing a predictive model with good performance. …"

Table 3_Integrative single-cell and spatial transcriptomics analysis reveals FLAD1 as a regulator of the immune microenvironment in hepatocellular carcinoma.xlsx حسب Peng Zhu (277243)

"…A modeling approach using 92 combinations of nine machine learning algorithms was applied, producing a predictive model with good performance. …"

Table 7_Integrative single-cell and spatial transcriptomics analysis reveals FLAD1 as a regulator of the immune microenvironment in hepatocellular carcinoma.xlsx حسب Peng Zhu (277243)

"…A modeling approach using 92 combinations of nine machine learning algorithms was applied, producing a predictive model with good performance. …"

Table 10_Integrative single-cell and spatial transcriptomics analysis reveals FLAD1 as a regulator of the immune microenvironment in hepatocellular carcinoma.xlsx حسب Peng Zhu (277243)

"…A modeling approach using 92 combinations of nine machine learning algorithms was applied, producing a predictive model with good performance. …"

Image 2_Integrative single-cell and spatial transcriptomics analysis reveals FLAD1 as a regulator of the immune microenvironment in hepatocellular carcinoma.tif حسب Peng Zhu (277243)

"…A modeling approach using 92 combinations of nine machine learning algorithms was applied, producing a predictive model with good performance. …"

Table 5_Integrative single-cell and spatial transcriptomics analysis reveals FLAD1 as a regulator of the immune microenvironment in hepatocellular carcinoma.xlsx حسب Peng Zhu (277243)

"…A modeling approach using 92 combinations of nine machine learning algorithms was applied, producing a predictive model with good performance. …"

Image 3_Integrative single-cell and spatial transcriptomics analysis reveals FLAD1 as a regulator of the immune microenvironment in hepatocellular carcinoma.tif حسب Peng Zhu (277243)

"…A modeling approach using 92 combinations of nine machine learning algorithms was applied, producing a predictive model with good performance. …"

Table 2_Integrative single-cell and spatial transcriptomics analysis reveals FLAD1 as a regulator of the immune microenvironment in hepatocellular carcinoma.xlsx حسب Peng Zhu (277243)

"…A modeling approach using 92 combinations of nine machine learning algorithms was applied, producing a predictive model with good performance. …"

Table 6_Integrative single-cell and spatial transcriptomics analysis reveals FLAD1 as a regulator of the immune microenvironment in hepatocellular carcinoma.xlsx حسب Peng Zhu (277243)

"…A modeling approach using 92 combinations of nine machine learning algorithms was applied, producing a predictive model with good performance. …"

Data Sheet 1_Identification of ALDH2 as a novel target for the treatment of acute kidney injury in kidney transplantation based on WGCNA and machine learning algorithms and explora... حسب Jinpu Peng (748597)

"…</p>Methods<p>Based on the kidney transplantation AKI-related dataset GSE30718, the most relevant modular genes for AKI among them were firstly screened using WGCNA and intersected with the DEGs, and the intersected genes were used as candidate genes for kidney transplantation AKI. Second, machine learning algorithms were utilized to identify the key genes among them, and the HPA database was used to explore the expression landscape. …"

Data Sheet 1_A risk prediction model for poor joint function recovery after ankle fracture surgery based on interpretable machine learning.pdf حسب Congyang Li (10012877)

"…Objective<p>Currently, there is no individualized prediction model for joint function recovery after ankle fracture surgery. This study aims to develop a prediction model for poor recovery following ankle fracture surgery using various machine learning algorithms to facilitate early identification of high-risk patients.…"

DATASET: Data-Driven Clinical Decision Trees and Generative Topographic Mapping to Guide Stroke Rehabilitation Program Allocation حسب Dimitrios-Sokratis Komaris (10266358)

A. The boxplot displays the distribution of enrichment scores for disease-related immune functions between the control group and the experimental group. حسب Min-xiao Wang (20868922)

Machine Learning Models for Efficient Property Prediction of ABX₃ Materials: A High-Throughput Approach حسب Soundous Touati (20282599)

"…Through these predictive models, machine learning accelerates the exploration of new materials with enhanced performance and functionality.…"

Machine Learning Models for Efficient Property Prediction of ABX₃ Materials: A High-Throughput Approach حسب Soundous Touati (20282599)

"…Through these predictive models, machine learning accelerates the exploration of new materials with enhanced performance and functionality.…"

Machine Learning Models for Efficient Property Prediction of ABX₃ Materials: A High-Throughput Approach حسب Soundous Touati (20282599)

"…Through these predictive models, machine learning accelerates the exploration of new materials with enhanced performance and functionality.…"

Elastin-Based Janus Hydrogel Tape with Adhesive, Stretchable, and Conductive Properties for Soft Bioelectronic Applications حسب Zhongwei Guo (240554)

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Table 2_A machine learning-derived immune-related prognostic model identifies PLXNA3 as a functional risk gene in colorectal cancer.xlsx حسب Hanzhang Lyu (22163404)

"…</p>Methods<p>To address this gap, we constructed a robust prognostic model by integrating over 100 machine learning algorithms—such as LASSO, CoxBoost, and StepCox—based on transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts.…"

Image 3_A machine learning-derived immune-related prognostic model identifies PLXNA3 as a functional risk gene in colorectal cancer.tif حسب Hanzhang Lyu (22163404)

"…</p>Methods<p>To address this gap, we constructed a robust prognostic model by integrating over 100 machine learning algorithms—such as LASSO, CoxBoost, and StepCox—based on transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts.…"

Image 1_A machine learning-derived immune-related prognostic model identifies PLXNA3 as a functional risk gene in colorectal cancer.tif حسب Hanzhang Lyu (22163404)

"…</p>Methods<p>To address this gap, we constructed a robust prognostic model by integrating over 100 machine learning algorithms—such as LASSO, CoxBoost, and StepCox—based on transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts.…"