DataSheet1_FMixFN: A Fast Big Data-Oriented Genomic Selection Model Based on an Iterative Conditional Expectation algorithm.xlsx by Wenwu Xu (2023315)

DataSheet2_FMixFN: A Fast Big Data-Oriented Genomic Selection Model Based on an Iterative Conditional Expectation algorithm.doc by Wenwu Xu (2023315)

Relative performance of classification algorithms using gene-expression and clinical predictors and performing feature selection. by Stephen R. Piccolo (8367780)

“…We used nested cross validation to estimate which features would be optimal for each algorithm in each training set. For each combination of dataset, class variable, and classification algorithm, we calculated the arithmetic mean of area under the receiver operating characteristic curve (AUROC) values across 5 iterations of Monte Carlo cross-validation. …”

Convergence comparison of APDCA, SIMCLDA, MLFDA, and SDLDA over 100 iterations using relative error as the evaluation metric. by Yangsong He (13909371)

Summary of steps in iterative point set registration for scRNA-seq data. by Amir Alavi (9569337)

Summary of predictive performance per dataset when using gene-expression and clinical predictors and performing hyperparameter optimization. by Stephen R. Piccolo (8367780)

“…For classification algorithms that included multiple hyperparameter combinations (n = 47), we performed hyperparameter optimization using the respective training sets. …”

Summary of predictive performance per dataset when using gene-expression predictors. by Stephen R. Piccolo (8367780)

Summary of predictive performance per dataset when using gene-expression and clinical predictors. by Stephen R. Piccolo (8367780)

Comparison of ranks for classification algorithms across performance metrics. by Stephen R. Piccolo (8367780)

Tradeoff between execution time and predictive performance for classification algorithms. by Stephen R. Piccolo (8367780)

Summary of predictive performance per dataset when using gene-expression and clinical predictors and performing feature selection. by Stephen R. Piccolo (8367780)

Illustration of the BRIDES algorithm and the selection process. by Cindy Bouchard (6844979)

Simulated Design–Build–Test–Learn Cycles for Consistent Comparison of Machine Learning Methods in Metabolic Engineering by Paul van Lent (16876977)

“…In this work, we propose a mechanistic kinetic model-based framework to test and optimize machine learning for iterative combinatorial pathway optimization. …”

Table1_A depth-first search algorithm for oligonucleotide design in gene assembly.DOCX by Hanjie Liang (12253847)

“…Based on these fragments, a set of oligonucleotides for gene assembly is produced. …”

The operating principle of APDCA. by Yangsong He (13909371)

MOA classification performance and model benchmarking. by Josh L. Espinoza (10492448)

“…Optimization step (<i>t</i> = 0) corresponds to using all available gene features, while each optimization step removes low information features during each consecutive iteration. …”

The brief description on the WTCCC dataset. by Liyan Sun (760586)

“…Epi-SSA draws inspiration from the sparrow search algorithm and optimizes the population based on multiple objective functions in each iteration, in order to be able to more precisely identify epistatic interactions.…”

The penetrance tables for the 8 DNME models. by Liyan Sun (760586)

“…Epi-SSA draws inspiration from the sparrow search algorithm and optimizes the population based on multiple objective functions in each iteration, in order to be able to more precisely identify epistatic interactions.…”

The penetrance tables for the 8 DME models. by Liyan Sun (760586)

“…Epi-SSA draws inspiration from the sparrow search algorithm and optimizes the population based on multiple objective functions in each iteration, in order to be able to more precisely identify epistatic interactions.…”

The penetrance tables for the 6 DNME3 models. by Liyan Sun (760586)

“…Epi-SSA draws inspiration from the sparrow search algorithm and optimizes the population based on multiple objective functions in each iteration, in order to be able to more precisely identify epistatic interactions.…”