Optimization outcome for the elongation problem. by Shikun Chen (14625352)

“…The approach leverages gradient information from neural networks to guide SLSQP optimization while maintaining XGBoost’s prediction precision. …”

Table 1_Integrating GWAS and machine learning for disease risk prediction in the Taiwanese Hakka population.xlsx by Jing-Hong Xiao (22780781)

“…Integrating machine learning with GWAS offers a path to improve risk prediction and uncover functional variants relevant to precision medicine.</p>Methods<p>DNA samples from Taiwanese Hakka individuals with type 2 diabetes, hypertension, and eye diseases were analyzed. …”

Supplementary file 1_Integrating GWAS and machine learning for disease risk prediction in the Taiwanese Hakka population.docx by Jing-Hong Xiao (22780781)

“…Integrating machine learning with GWAS offers a path to improve risk prediction and uncover functional variants relevant to precision medicine.</p>Methods<p>DNA samples from Taiwanese Hakka individuals with type 2 diabetes, hypertension, and eye diseases were analyzed. …”

Bayesian Clustering via Fusing of Localized Densities by Alexander Dombowsky (20289372)

“…After defining a prior for the component parameters and weights, Markov chain Monte Carlo (MCMC) algorithms are commonly used to produce samples from the posterior distribution of the component labels. …”

The dataset comprises two-level price and sales data generated for numerical experiments. by Minchao Zheng (20558832)

Descriptions of parameters and symbols (<i>t</i> = 1,2). by Minchao Zheng (20558832)

“…A discrete algorithm is proposed to approximate the optimal solution, with its convergence rigorously proven. …”

Solution of approximate optimal order quantity. by Minchao Zheng (20558832)

“…A discrete algorithm is proposed to approximate the optimal solution, with its convergence rigorously proven. …”

Data Sheet 1_Assessment of shoulder functional movements through inertial measurement units for tele-rehabilitation: a quaternion-based approach.pdf by Matteo Iurato (22155370)

“…Data from an IMU-based device were acquired during the execution of human functional shoulder movements by both a young and elderly group of participants. …”

Data analyzed for the article of <b>Evaluating photoplethysmography-based pulsewave parameters and composite scores for assessment of cardiac function: A comparison with echocardio... by Kulin (20907929)

“…Concurrently, echocardiographic parameters were derived by averaging the data from 1-3 heartbeats, allowing for a direct comparison of cardiac function assessments between the two techniques, by the following. …”

IEEE Big Data 2024 Presentation: Norma: A Framework for Finding Threshold Associations Between Continuous Variables Using Point-wise Function by Md Mahin (21434966)

“…Norma associates point-wise functions with each variable-e.g., a function that returns poverty rates for each location in New Mexico. …”

Table 1_Differential gene expression profiling and machine learning-based discovery of key genetic markers in VTE and CKD.docx by Hui Li (32376)

“…Functional enrichment analyses were performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. …”

Table 6_Pain - related methylation driver genes affect the prognosis of pancreatic cancer patients by altering immune function and perineural infiltration.xlsx by Chencheng Zhang (6877691)

“…</p>Methods<p>Integrating multi-omics data from TCGA-PAAD (Pancreatic adenocarcinoma), we identified methylation driver genes (MDGs) using the MethylMix algorithm. …”

Table 2_Pain - related methylation driver genes affect the prognosis of pancreatic cancer patients by altering immune function and perineural infiltration.xlsx by Chencheng Zhang (6877691)

“…</p>Methods<p>Integrating multi-omics data from TCGA-PAAD (Pancreatic adenocarcinoma), we identified methylation driver genes (MDGs) using the MethylMix algorithm. …”

Table 3_Pain - related methylation driver genes affect the prognosis of pancreatic cancer patients by altering immune function and perineural infiltration.xlsx by Chencheng Zhang (6877691)

“…</p>Methods<p>Integrating multi-omics data from TCGA-PAAD (Pancreatic adenocarcinoma), we identified methylation driver genes (MDGs) using the MethylMix algorithm. …”

Table 1_Pain - related methylation driver genes affect the prognosis of pancreatic cancer patients by altering immune function and perineural infiltration.xlsx by Chencheng Zhang (6877691)

“…</p>Methods<p>Integrating multi-omics data from TCGA-PAAD (Pancreatic adenocarcinoma), we identified methylation driver genes (MDGs) using the MethylMix algorithm. …”

Table 4_Pain - related methylation driver genes affect the prognosis of pancreatic cancer patients by altering immune function and perineural infiltration.xlsx by Chencheng Zhang (6877691)

“…</p>Methods<p>Integrating multi-omics data from TCGA-PAAD (Pancreatic adenocarcinoma), we identified methylation driver genes (MDGs) using the MethylMix algorithm. …”

Table 5_Pain - related methylation driver genes affect the prognosis of pancreatic cancer patients by altering immune function and perineural infiltration.xlsx by Chencheng Zhang (6877691)

“…</p>Methods<p>Integrating multi-omics data from TCGA-PAAD (Pancreatic adenocarcinoma), we identified methylation driver genes (MDGs) using the MethylMix algorithm. …”

DEGs. by Rui Guo (134395)

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)

“…Its restrictions block GEP from successfully handling high-dimensional along with complex optimization problems. …”