Table 1_Composition-centered prediction of kenaf core saccharification for next-generation bioethanol via machine learning.docx حسب Yitong Niu (22658927)

"…The curated dataset (n = 35) was used to train Random-Forest regressors tuned by six hyperparameter optimizers (grid search, random search, Bayesian optimization, genetic algorithm, particle swarm optimization, and simulated annealing). …"

Delineating Structural Propensities of the 4E-BP2 Protein via Integrative Modeling and Clustering حسب Thomas E. Tsangaris (16846725)

"…Here, we used a Rosetta-based sampling algorithm optimized for IDRs to generate initial ensembles for two phospho forms of 4E-BP2, non- and 5-fold phosphorylated (NP and 5P, respectively), with the 5P folded domain flanked by N- and C-terminal IDRs (N-IDR and C-IDR, respectively). …"

Delineating Structural Propensities of the 4E-BP2 Protein via Integrative Modeling and Clustering حسب Thomas E. Tsangaris (16846725)

"…Here, we used a Rosetta-based sampling algorithm optimized for IDRs to generate initial ensembles for two phospho forms of 4E-BP2, non- and 5-fold phosphorylated (NP and 5P, respectively), with the 5P folded domain flanked by N- and C-terminal IDRs (N-IDR and C-IDR, respectively). …"

Stochastic Low-rank Tensor Bandits for Multi-dimensional Online Decision Making حسب Jie Zhou (28945)

"…Furthermore, we develop a practically effective Bayesian algorithm called tensor ensemble sampling for tensor bandits with context. …"

Data_Sheet_2_Feature Selection for Breast Cancer Classification by Integrating Somatic Mutation and Gene Expression.docx حسب Qin Jiang (503001)

"…In the stage of feature selection, we propose a staged feature selection algorithm, using fold change, false discovery rate to select differentially expressed genes, mutual information to remove the irrelevant and redundant features, and the embedded method based on gradient boosting decision tree with Bayesian optimization to obtain an optimal model. …"

Data_Sheet_1_Feature Selection for Breast Cancer Classification by Integrating Somatic Mutation and Gene Expression.CSV حسب Qin Jiang (503001)

"…In the stage of feature selection, we propose a staged feature selection algorithm, using fold change, false discovery rate to select differentially expressed genes, mutual information to remove the irrelevant and redundant features, and the embedded method based on gradient boosting decision tree with Bayesian optimization to obtain an optimal model. …"

Distribution on Warp Maps for Alignment of Open and Closed Curves حسب Karthik Bharath (3203817)

"…We demonstrate its utility by using it as a prior distribution on warp maps in a Bayesian model for alignment of two univariate curves, and as a proposal distribution in a stochastic algorithm that optimizes a suitable alignment functional for higher-dimensional curves. …"

Data Sheet 1_Beyond the current state of just-in-time adaptive interventions in mental health: a qualitative systematic review.pdf حسب Claire R. van Genugten (20626733)

"…To accomplish this, JITAIs often apply complex analytic techniques, such as machine learning or Bayesian algorithms to real- or near-time data acquired from smartphones and other sensors. …"

DataSheet_2_Optimising Treatment Outcomes for Children and Adults Through Rapid Genome Sequencing of Sepsis Pathogens. A Study Protocol for a Prospective, Multi-Centre Trial (DIREC... حسب Adam D. Irwin (11011752)

"…This pilot study will yield key feasibility data to inform further, urgently needed sepsis studies. …"

DataSheet_1_Optimising Treatment Outcomes for Children and Adults Through Rapid Genome Sequencing of Sepsis Pathogens. A Study Protocol for a Prospective, Multi-Centre Trial (DIREC... حسب Adam D. Irwin (11011752)

"…This pilot study will yield key feasibility data to inform further, urgently needed sepsis studies. …"

Table_3_Feature Selection for Breast Cancer Classification by Integrating Somatic Mutation and Gene Expression.XLSX حسب Qin Jiang (503001)

"…In the stage of feature selection, we propose a staged feature selection algorithm, using fold change, false discovery rate to select differentially expressed genes, mutual information to remove the irrelevant and redundant features, and the embedded method based on gradient boosting decision tree with Bayesian optimization to obtain an optimal model. …"

Image_2_Feature Selection for Breast Cancer Classification by Integrating Somatic Mutation and Gene Expression.JPEG حسب Qin Jiang (503001)

"…In the stage of feature selection, we propose a staged feature selection algorithm, using fold change, false discovery rate to select differentially expressed genes, mutual information to remove the irrelevant and redundant features, and the embedded method based on gradient boosting decision tree with Bayesian optimization to obtain an optimal model. …"

Image_1_Feature Selection for Breast Cancer Classification by Integrating Somatic Mutation and Gene Expression.JPEG حسب Qin Jiang (503001)

"…In the stage of feature selection, we propose a staged feature selection algorithm, using fold change, false discovery rate to select differentially expressed genes, mutual information to remove the irrelevant and redundant features, and the embedded method based on gradient boosting decision tree with Bayesian optimization to obtain an optimal model. …"

Table_2_Feature Selection for Breast Cancer Classification by Integrating Somatic Mutation and Gene Expression.XLSX حسب Qin Jiang (503001)

"…In the stage of feature selection, we propose a staged feature selection algorithm, using fold change, false discovery rate to select differentially expressed genes, mutual information to remove the irrelevant and redundant features, and the embedded method based on gradient boosting decision tree with Bayesian optimization to obtain an optimal model. …"