Image 2_A machine learning-derived immune-related prognostic model identifies PLXNA3 as a functional risk gene in colorectal cancer.tif by 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 by 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 by 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 by 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 by 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.…”

Classification performances of Ensemble, SVM, and kNN algorithms. For each performance metric, the mean value across all runs and standard deviation of the mean value is reported i... by Ayşenur Eser (21713911)

Demonstration of the limitations of existing spatial analysis methods, namely nearest neighbor distance (NND), G-cross function and Morisita-Horn index. by Mai Chan Lau (3135303)

Uncertainty and Novelty in Machine Learning by Derek Scott Prijatelj (20364288)

“…<p>Uncertainty and novelty are inherent in machine learning, especially as new information is encountered and the hypothesis set’s best model is to be determined given the current information. …”

Data Sheet 1_Association between red blood cell distribution width-to-albumin ratio and in-hospital mortality in patients with congestive heart failure combined with chronic kidney... by Lie-jun Qian (21659372)

“…Correlation analysis and machine learning algorithms were used to screen the clinical features associated with RAR. …”

A-B. The boxplots present the distribution of scores for IRF4 and ELANE immune-related functions in low-expression and high-expression groups. by Min-xiao Wang (20868922)

PyNoetic’s classification module supports a range of popular and widely used ML classification models with just a single line of function call. by Gursimran Singh (575288)

Subjects:

Differences in computing performance between ENQUIRE’s gene normalization algorithm and GNorm2-Bioformer. by Luca Musella (19986312)

Streamfunction calculated by QA with different with different grid resolutions Streamfunction averaged over 10 runs from QA (shading) and the “true” solution (black contour lines)... by Takuro Matsuta (21608505)

Subjects: “…currently available algorithms…”

QA and SA solutions by truncated spectral expansion when by Takuro Matsuta (21608505)

Subjects: “…currently available algorithms…”

Streamfunction calculated by SA with different hyperparameters. by Takuro Matsuta (21608505)

Subjects: “…currently available algorithms…”

QA and SA solutions by truncated spectral expansion when by Takuro Matsuta (21608505)

Subjects: “…currently available algorithms…”

Schematics for graph embedding. by Takuro Matsuta (21608505)

Subjects: “…currently available algorithms…”

SA solutions using a series of spins without iteration. by Takuro Matsuta (21608505)

Subjects: “…currently available algorithms…”

Schematics of SA and QA procedure. by Takuro Matsuta (21608505)

Subjects: “…currently available algorithms…”

QA solutions using a series of spins without iteration. by Takuro Matsuta (21608505)

Subjects: “…currently available algorithms…”