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

الموضوعات:

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.…"

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)

الموضوعات:

Spatial characterization of cancer-associated cell types in human breast cancer malignancies. حسب Nuray Sogunmez Erdogan (21533872)

الموضوعات:

Precise spatial delineation of brain cell types in adult mouse brain. حسب Nuray Sogunmez Erdogan (21533872)

الموضوعات:

Deconvolution challenges due to coherence and heterogeneity. حسب Nuray Sogunmez Erdogan (21533872)

الموضوعات:

Pictorial representation of isomorphically scaling down spots within a fixed field of view and corresponding imaging configuration. حسب Nicholas H. Vitale (20469289)

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

الموضوعات:

Single-cell RNA sequencing analysis reveals the expression patterns of the prognostic signature across cellular subpopulations. حسب Xiangxin Wu (22320058)

الموضوعات:

A detailed process of iterative simulation coupled with bone density algorithm; (a) a function of stimulus and related bone density changes, and (b) iterative calculations of finit... حسب Hassan Mehboob (8960273)

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

الموضوعات: