بدائل البحث:
action optimization » reaction optimization (توسيع البحث), function optimization (توسيع البحث), codon optimization (توسيع البحث)
all optimization » art optimization (توسيع البحث), ai optimization (توسيع البحث), whale optimization (توسيع البحث)
binary based » library based (توسيع البحث), linac based (توسيع البحث), binary mask (توسيع البحث)
based action » based motion (توسيع البحث), based active (توسيع البحث), based fusion (توسيع البحث)
based all » based small (توسيع البحث), based cell (توسيع البحث), based ap (توسيع البحث)
action optimization » reaction optimization (توسيع البحث), function optimization (توسيع البحث), codon optimization (توسيع البحث)
all optimization » art optimization (توسيع البحث), ai optimization (توسيع البحث), whale optimization (توسيع البحث)
binary based » library based (توسيع البحث), linac based (توسيع البحث), binary mask (توسيع البحث)
based action » based motion (توسيع البحث), based active (توسيع البحث), based fusion (توسيع البحث)
based all » based small (توسيع البحث), based cell (توسيع البحث), based ap (توسيع البحث)
-
1
Relative performance of classification algorithms using gene-expression and clinical predictors and performing feature selection.
منشور في 2022"…Next, we sorted the algorithms based on the average rank across all dataset/class combinations. …"
-
2
DataSheet1_Establishment and Optimization of Radiomics Algorithms for Prediction of KRAS Gene Mutation by Integration of NSCLC Gene Mutation Mutual Exclusion Information.DOCX
منشور في 2022"…<p>Purpose: To assess the significance of mutation mutual exclusion information in the optimization of radiomics algorithms for predicting gene mutation.…"
-
3
-
4
-
5
-
6
-
7
Table3_Comprehensive analysis of the progression mechanisms of CRPC and its inhibitor discovery based on machine learning algorithms.XLSX
منشور في 2023"…Weighted gene coexpression network analysis (WGCNA) and two machine learning algorithms were employed to identify potential biomarkers for CRPC. …"
-
8
Table2_Comprehensive analysis of the progression mechanisms of CRPC and its inhibitor discovery based on machine learning algorithms.XLSX
منشور في 2023"…Weighted gene coexpression network analysis (WGCNA) and two machine learning algorithms were employed to identify potential biomarkers for CRPC. …"
-
9
Table1_Comprehensive analysis of the progression mechanisms of CRPC and its inhibitor discovery based on machine learning algorithms.XLSX
منشور في 2023"…Weighted gene coexpression network analysis (WGCNA) and two machine learning algorithms were employed to identify potential biomarkers for CRPC. …"
-
10
-
11
DataSheet1_Comprehensive analysis of immune-related gene signature based on ssGSEA algorithms in the prognosis and immune landscape of hepatocellular carcinoma.ZIP
منشور في 2022"…</p><p>Methods: Transcriptomic data of patients with HCC were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We assessed the immune cell infiltration in each HCC specimen using single sample gene set enrichment analysis (ssGSEA) and classified all patients with HCC into high- and low-immune clusters using a hierarchical clustering algorithm. …"
-
12
-
13
-
14
-
15
-
16
Table1_Identification of a ferroptosis-related gene signature predicting recurrence in stage II/III colorectal cancer based on machine learning algorithms.XLSX
منشور في 2023"…We developed a machine learning framework with 83 combinations of 10 algorithms based on 10-fold cross-validation (CV) or bootstrap resampling algorithm to identify the most robust and stable model. …"
-
17
DataSheet1_Identification of a ferroptosis-related gene signature predicting recurrence in stage II/III colorectal cancer based on machine learning algorithms.DOCX
منشور في 2023"…We developed a machine learning framework with 83 combinations of 10 algorithms based on 10-fold cross-validation (CV) or bootstrap resampling algorithm to identify the most robust and stable model. …"
-
18
-
19
-
20
DataSheet_1_Trans-population graph-based coverage optimization of allogeneic cellular therapy.xlsx
منشور في 2023"…We propose here a solution to this problem, and test whether it would be more expensive to recruit additional donors or to prevent class I or class II HLA expression through gene editing.</p>Study design<p>We developed an optimal coverage problem, combined with a graph-based algorithm to solve the donor selection problem under different, clinically plausible scenarios (having different HLA matching priorities). …"