Sensor value scenario for fuzzy logic algorithm. حسب Gourab Saha (8987405)

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Sensor value scenario for average value algorithm. حسب Gourab Saha (8987405)

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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.…"

Image 2_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 7_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 6_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 5_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 4_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.…"

Distribution of cross correlations in functional connectivity in ABIDE sample. حسب Alexandria M. Jensen (6494957)

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Predicting the Mutagenic Activity of Nitroaromatics Using Conceptual Density Functional Theory Descriptors and Explainable No-Code Machine Learning Approaches حسب Andrés Halabi Diaz (20798460)

"…This study integrates conceptual density functional theory (CDFT) descriptors with explainable no-code machine learning (ML) models to predict NA mutagenicity based on Ames test results. …"

Pipeline of the interactor algorithm. حسب Jose Cleydson F. Silva (8685594)

Dataset for 'An Automated Workflow of Deep Learning Ensemble Model with Principal Component Analysis for Enhanced Material Characterisation in Energy Dispersive Spectroscopy' حسب Fanzhi Su (21005174)

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Unsupervised Machine Learning for Differential Analysis in Proteomics حسب Guanyang Xu (22379531)

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Explained variance ration of the PCA algorithm. حسب Abeer Aljohani (18497914)

الموضوعات:

The performance results on benchmark test functions and real-world problems. حسب Mahmood A. Jumaah (22272371)

الموضوعات:

Functional analysis of anoikis-related genes. حسب Yufeng He (5673119)

الموضوعات:

Flowchart of a single epoch in the FOX optimization algorithm [10]. حسب Mahmood A. Jumaah (22272371)

الموضوعات:

Adaptive exploration and exploitation method of Improved FOX optimization algorithm. حسب Mahmood A. Jumaah (22272371)

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