IOWarp v1.0 Beta: Advancing Scientific AI with Intelligent Data Management & MCP Integration by Anthony Kougkas (17069216)

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

Image and optimization comparison diagram for the Hartmann 4-D function. by Kuiyuan Pan (21145338)

“…<p>(a) Hartmann 4-D three-dimensional function image. …”

Image and optimization comparison diagram for the Schaffer F7 function. by Kuiyuan Pan (21145338)

“…<p>(a) Schaffer F7 three-dimensional function image. …”

The coregulatory network analysis of the hub genes from the study. by Devi Soorya Narayana Sasikumar (22331743)

PATH⁺ correctly predicted a weaker binding affinity for HIV-1 protease with the drug-resistant G48V mutation (right, experimental kcal/mol, PDB ID: 3cyw [81]) bound to d... by Yuxi Long (11024307)

Vegetation mapping for Feb and Jun 2019 from the year 2015 to 2019. by Gourab Saha (8987405)

Comparison of optimization performance of six ABC algorithms. by Shuhui Yu (1535500)

Impact of different Copula functions and correlation strengths on optimal order quantities. by Minchao Zheng (20558832)

Analyses of Immune microenvironment and functions between gene clusters by Jiangli Zhao (2308330)

DEGs. by Rui Guo (134395)

The list of 434 anoikis-related genes (ARGs). by Yanfeng Xue (465345)

“…We employed univariate Cox regression analysis, LASSO regression, and random forest algorithms to identify anoikis-related genes (ARG) from bulk transcriptomic datasets, and establish a 7-gene prognostic signature, validated in two LUAD cohorts from GEO database. …”

Workflow diagram for this study. by Yanfeng Xue (465345)

“…We employed univariate Cox regression analysis, LASSO regression, and random forest algorithms to identify anoikis-related genes (ARG) from bulk transcriptomic datasets, and establish a 7-gene prognostic signature, validated in two LUAD cohorts from GEO database. …”

TIMP1 gene was highly expressed in LUAD tissues and regulated malignant cell- Treg interactions. by Yanfeng Xue (465345)

Test data on the ability to escape local optima. by Kejia Liu (5699651)

“…The study employed traditional benchmark functions and conducted evaluations versus baselines Standard GEP, NMO-SARA, and MS-GEP-A to assess fitness outcomes, R² values, population diversification, and the avoidance of local optima. …”

Summary of the notations. by Kejia Liu (5699651)

“…The study employed traditional benchmark functions and conducted evaluations versus baselines Standard GEP, NMO-SARA, and MS-GEP-A to assess fitness outcomes, R² values, population diversification, and the avoidance of local optima. …”

Comparison of population diversity. by Kejia Liu (5699651)

“…The study employed traditional benchmark functions and conducted evaluations versus baselines Standard GEP, NMO-SARA, and MS-GEP-A to assess fitness outcomes, R² values, population diversification, and the avoidance of local optima. …”

Test data on mining capacity. by Kejia Liu (5699651)

“…The study employed traditional benchmark functions and conducted evaluations versus baselines Standard GEP, NMO-SARA, and MS-GEP-A to assess fitness outcomes, R² values, population diversification, and the avoidance of local optima. …”

Comparison of standard GEP and DGEP. by Kejia Liu (5699651)

“…The study employed traditional benchmark functions and conducted evaluations versus baselines Standard GEP, NMO-SARA, and MS-GEP-A to assess fitness outcomes, R² values, population diversification, and the avoidance of local optima. …”

Test data on population diversity. by Kejia Liu (5699651)

“…The study employed traditional benchmark functions and conducted evaluations versus baselines Standard GEP, NMO-SARA, and MS-GEP-A to assess fitness outcomes, R² values, population diversification, and the avoidance of local optima. …”