The IALO algorithm solution flowchart. by Wenjing Wang (181404)

“…The average running time of the proposed algorithm in Sphere function and Griebank function was 2.67s and 1.64s, respectively. …”

Variance of local fields versus mean of local fields for (a) , (b) . by Ali Talebi (7164203)

Box plots of the fitness function across independent runs. by Jin Wang (29560)

(a-b) The LASSO algorithm was used for hyperparameter tuning and identification of the important predictive features. by Devi Soorya Narayana Sasikumar (22331743)

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Features of 13 pSRGs obtained by machine learning methods. by Xihao Shen (20347942)

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Data Sheet 4_Identification of a signature gene set for oxaliplatin sensitivity prediction in colorectal cancer.pdf by Xiaopeng Zhan (4170574)

“…</p>Methods<p>Here, we conducted a comprehensive analysis of large-scale genomic datasets, including from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Machine learning algorithms to these datasets was applied to identify genes associated with oxaliplatin response. …”

Data Sheet 1_Identification of a signature gene set for oxaliplatin sensitivity prediction in colorectal cancer.pdf by Xiaopeng Zhan (4170574)

“…</p>Methods<p>Here, we conducted a comprehensive analysis of large-scale genomic datasets, including from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Machine learning algorithms to these datasets was applied to identify genes associated with oxaliplatin response. …”

Table 1_Identification of a signature gene set for oxaliplatin sensitivity prediction in colorectal cancer.xlsx by Xiaopeng Zhan (4170574)

“…</p>Methods<p>Here, we conducted a comprehensive analysis of large-scale genomic datasets, including from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Machine learning algorithms to these datasets was applied to identify genes associated with oxaliplatin response. …”

Data Sheet 2_Identification of a signature gene set for oxaliplatin sensitivity prediction in colorectal cancer.pdf by Xiaopeng Zhan (4170574)

“…</p>Methods<p>Here, we conducted a comprehensive analysis of large-scale genomic datasets, including from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Machine learning algorithms to these datasets was applied to identify genes associated with oxaliplatin response. …”

Image 1_Identification of a signature gene set for oxaliplatin sensitivity prediction in colorectal cancer.pdf by Xiaopeng Zhan (4170574)

“…</p>Methods<p>Here, we conducted a comprehensive analysis of large-scale genomic datasets, including from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Machine learning algorithms to these datasets was applied to identify genes associated with oxaliplatin response. …”

Data Sheet 3_Identification of a signature gene set for oxaliplatin sensitivity prediction in colorectal cancer.pdf by Xiaopeng Zhan (4170574)

“…</p>Methods<p>Here, we conducted a comprehensive analysis of large-scale genomic datasets, including from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Machine learning algorithms to these datasets was applied to identify genes associated with oxaliplatin response. …”

Machine learning models. by Meili Liu (327309)

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Automatic translation error detection based on fuzzy decision tree algorithm. by Danqiangyu Zhou (22146196)

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The impact of extrinsic noise sources on the measured SNR is plotted for both CW and TG against varying optical filter magnitudes. by Nicholas H. Vitale (20469289)

Data XGBOOST. by Xin Zhang (35492)

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Descriptions of the selected CEC 2017 benchmark functions. by ZeSheng Lin (20501356)

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Descriptions of the selected CEC 2005 benchmark functions. by ZeSheng Lin (20501356)

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Descriptions of the selected CEC 2022 benchmark functions. by ZeSheng Lin (20501356)

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Analysis of ABIDE dataset: Conventional regression frameworks for optimized algorithmic value. by Alexandria M. Jensen (6494957)

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PSO parameters and objective function. by André G. Coimbra (21354135)