Search alternatives:
reaction optimization » production optimization (Expand Search), rational optimization (Expand Search), generation optimization (Expand Search)
smart optimization » swarm optimization (Expand Search), art optimization (Expand Search), whale optimization (Expand Search)
based reaction » based action (Expand Search), based prediction (Expand Search), based detection (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
based smart » based sars (Expand Search), based search (Expand Search)
reaction optimization » production optimization (Expand Search), rational optimization (Expand Search), generation optimization (Expand Search)
smart optimization » swarm optimization (Expand Search), art optimization (Expand Search), whale optimization (Expand Search)
based reaction » based action (Expand Search), based prediction (Expand Search), based detection (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
based smart » based sars (Expand Search), based search (Expand Search)
-
1
Table1_A depth-first search algorithm for oligonucleotide design in gene assembly.DOCX
Published 2022“…Based on these fragments, a set of oligonucleotides for gene assembly is produced. …”
-
2
Data_Sheet_1_Transcriptome-Based Selection and Validation of Reference Genes for Gene Expression Analysis of Alicyclobacillus acidoterrestris Under Acid Stress.PDF
Published 2021“…The expression stability of eight new RGs and commonly used RG 16s rRNA was assessed using geNorm, NormFinder, and BestKeeper algorithms. Moreover, the comprehensive analysis using the RefFinder program and the validation using target gene ctsR showed that dnaG and dnaN were the optimal multiple RGs for normalization at pH 4.0; ytvI, dnaG, and 16s rRNA at pH 3.5; icd and dnaG at pH 3.0; and ytvI, dnaG, and spoVE at pH 2.5. …”
-
3
Data_Sheet_2_Transcriptome-Based Selection and Validation of Reference Genes for Gene Expression Analysis of Alicyclobacillus acidoterrestris Under Acid Stress.xls
Published 2021“…The expression stability of eight new RGs and commonly used RG 16s rRNA was assessed using geNorm, NormFinder, and BestKeeper algorithms. Moreover, the comprehensive analysis using the RefFinder program and the validation using target gene ctsR showed that dnaG and dnaN were the optimal multiple RGs for normalization at pH 4.0; ytvI, dnaG, and 16s rRNA at pH 3.5; icd and dnaG at pH 3.0; and ytvI, dnaG, and spoVE at pH 2.5. …”
-
4
-
5
Data Sheet 1_Clinical potential and experimental validation of prognostic genes in hepatocellular carcinoma revealed by risk modeling utilizing single cell and transcriptome constr...
Published 2025“…</p>Methods<p>The HCC datasets were obtained from public databases and then differential expression analysis were used to obtain significant gene expression profiles. Subsequently, univariate Cox regression analysis and PH assumption test were performed, and a risk model was developed using an optimal algorithm from 101 combinations on the TCGA-LIHC dataset to pinpoint prognostic genes. …”
-
6
-
7
DataSheet_1_A novel prognostic model based on three integrin subunit genes-related signature for bladder cancer.docx
Published 2022“…This study endeavored to thoroughly analyze the utility of ITGs in BLCA through computer algorithm-based bioinformatics.</p>Methods<p>BLCA-related materials were sourced from reputable databases, The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). …”
-
8
-
9
-
10
DataSheet2_Prognostic N6-methyladenosine (m6A)-related lncRNA patterns to aid therapy in pancreatic ductal adenocarcinoma.pdf
Published 2022“…Gene set variation analysis (GSVA) was employed to assign pathway activity estimates to individual samples. …”
-
11
DataSheet1_Prognostic N6-methyladenosine (m6A)-related lncRNA patterns to aid therapy in pancreatic ductal adenocarcinoma.xlsx
Published 2022“…Gene set variation analysis (GSVA) was employed to assign pathway activity estimates to individual samples. …”
-
12
Supplementary file 1_Development and validation of machine learning-based diagnostic models using blood transcriptomics for early childhood diabetes prediction.xlsx
Published 2025“…Five feature selection methods (Lasso, Elastic Net, Random Forest, Support Vector Machine, and Gradient Boosting Machine) were employed to optimize gene sets. Nine machine learning algorithms (Decision Tree, Gradient Boosting Machine, K-Nearest Neighbors, Linear Discriminant Analysis, Logistic Regression, Multilayer Perceptron, Naive Bayes, Random Forest, and Support Vector Machine) were combined with selected features, generating 45 unique model combinations. …”
-
13
Table1_Analytical validation and clinical utilization of K-4CARE™: a comprehensive genomic profiling assay with personalized MRD detection.XLSX
Published 2024“…<p>Background: Biomarker testing has gradually become standard of care in precision oncology to help physicians select optimal treatment for patients. Compared to single-gene or small gene panel testing, comprehensive genomic profiling (CGP) has emerged as a more time- and tissue-efficient method. …”
-
14
Image_2_Construction of a novel choline metabolism-related signature to predict prognosis, immune landscape, and chemotherapy response in colon adenocarcinoma.tif
Published 2022“…</p>Methods<p>Choline metabolism-related differentially expressed genes (DEGs) between normal and COAD tissues were screened using datasets from The Cancer Genome Atlas (TCGA), Kyoto Encyclopedia of Genes and Genomes (KEGG), AmiGO2 and Reactome Pathway databases. …”
-
15
DataSheet_2_Construction of a novel choline metabolism-related signature to predict prognosis, immune landscape, and chemotherapy response in colon adenocarcinoma.csv
Published 2022“…</p>Methods<p>Choline metabolism-related differentially expressed genes (DEGs) between normal and COAD tissues were screened using datasets from The Cancer Genome Atlas (TCGA), Kyoto Encyclopedia of Genes and Genomes (KEGG), AmiGO2 and Reactome Pathway databases. …”
-
16
Image_1_Construction of a novel choline metabolism-related signature to predict prognosis, immune landscape, and chemotherapy response in colon adenocarcinoma.tiff
Published 2022“…</p>Methods<p>Choline metabolism-related differentially expressed genes (DEGs) between normal and COAD tissues were screened using datasets from The Cancer Genome Atlas (TCGA), Kyoto Encyclopedia of Genes and Genomes (KEGG), AmiGO2 and Reactome Pathway databases. …”
-
17
DataSheet_3_Construction of a novel choline metabolism-related signature to predict prognosis, immune landscape, and chemotherapy response in colon adenocarcinoma.csv
Published 2022“…</p>Methods<p>Choline metabolism-related differentially expressed genes (DEGs) between normal and COAD tissues were screened using datasets from The Cancer Genome Atlas (TCGA), Kyoto Encyclopedia of Genes and Genomes (KEGG), AmiGO2 and Reactome Pathway databases. …”
-
18
DataSheet_4_Construction of a novel choline metabolism-related signature to predict prognosis, immune landscape, and chemotherapy response in colon adenocarcinoma.csv
Published 2022“…</p>Methods<p>Choline metabolism-related differentially expressed genes (DEGs) between normal and COAD tissues were screened using datasets from The Cancer Genome Atlas (TCGA), Kyoto Encyclopedia of Genes and Genomes (KEGG), AmiGO2 and Reactome Pathway databases. …”
-
19
Table_2_Construction of a novel choline metabolism-related signature to predict prognosis, immune landscape, and chemotherapy response in colon adenocarcinoma.xlsx
Published 2022“…</p>Methods<p>Choline metabolism-related differentially expressed genes (DEGs) between normal and COAD tissues were screened using datasets from The Cancer Genome Atlas (TCGA), Kyoto Encyclopedia of Genes and Genomes (KEGG), AmiGO2 and Reactome Pathway databases. …”
-
20
Table_1_Construction of a novel choline metabolism-related signature to predict prognosis, immune landscape, and chemotherapy response in colon adenocarcinoma.docx
Published 2022“…</p>Methods<p>Choline metabolism-related differentially expressed genes (DEGs) between normal and COAD tissues were screened using datasets from The Cancer Genome Atlas (TCGA), Kyoto Encyclopedia of Genes and Genomes (KEGG), AmiGO2 and Reactome Pathway databases. …”