DataSheet1_A self-training subspace clustering algorithm based on adaptive confidence for gene expression data.PDF by Dan Li (106345)

“…The superiority of the proposed SSCAC algorithm is mainly reflected in the following aspects. 1) In order to improve the discriminative property of gene expression data, the low-rank representation with distance penalty is used to mine the potential subspace structure of data. 2) Considering the problem of mislabeling in self-training, a semi-supervised clustering objective function with label confidence is proposed, and a self-training subspace clustering framework is constructed on this basis. 3) In order to mitigate the negative impact of mislabeled data, an adaptive adjustment strategy based on gravitational search algorithm is proposed for label confidence. …”

Workflow from data acquisition to automated diagnostics. by Bernhard Vennemann (7434353)

SBM 2023 Poster: Development and Validation of Multivariable Prediction Algorithms to Estimate Future Walking Behavior in Adults: Retrospective Cohort Study by Junghwan Park (15195436)

“…The prediction performance of six algorithms was evaluated: logistic regression, radial-basis function support vector machine, eXtreme Gradient Boosting (XGBoost), multi-layered perceptron (MLP), decision tree, and random forest. …”

Comparison of details of different algorithms. by Hao Wu (65943)

“…In the network structure, the PS-UNet++ network is based on the sub-pixel convolution upsampling module, and the UNet++ network is constructed as the feature extraction sub-network of the optimization algorithm to extract more detailed information from the model. …”

Structure of the LMPEC algorithm model. by Hao Wu (65943)

“…In the network structure, the PS-UNet++ network is based on the sub-pixel convolution upsampling module, and the UNet++ network is constructed as the feature extraction sub-network of the optimization algorithm to extract more detailed information from the model. …”

Flexible CDOCKER: Hybrid Searching Algorithm and Scoring Function with Side Chain Conformational Entropy by Yujin Wu (2901128)

“…Finally, we select the T4 L99A and T4 L99A/M102Q decoy sets, containing dozens of binders and experimentally validated nonbinders, to test our approach in distinguishing binders from nonbinders. We illustrate that our new algorithms for searching and scoring have superior performance to rigid receptor CDOCKER as well as AutoDock Vina. …”

Flexible CDOCKER: Hybrid Searching Algorithm and Scoring Function with Side Chain Conformational Entropy by Yujin Wu (2901128)

“…Finally, we select the T4 L99A and T4 L99A/M102Q decoy sets, containing dozens of binders and experimentally validated nonbinders, to test our approach in distinguishing binders from nonbinders. We illustrate that our new algorithms for searching and scoring have superior performance to rigid receptor CDOCKER as well as AutoDock Vina. …”

Algorithm for matching a DICOM file obtained from PACS with a radiology report collected from HIS. by Thao Nguyen (51638)

Multidomain, Automated Photopatterning of DNA-functionalized Hydrogels (MAPDH). by Moshe Rubanov (7289156)

“…<b>B)</b> Pseudocode for MAPDH in Python. The algorithm takes as input the vials that will be flowed through the patterning chamber. …”

The iteration curve of the test function. by Jian Kong (164512)

“…The directional preference algorithm could further provide more valuable solutions on the basis of adaptive genetic algorithms. …”

table1_A Novel Strategy for Decoding and Validating the Combination Principles of Huanglian Jiedu Decoction From Multi-Scale Perspective.xlsx by Ke-Xin Wang (8890475)

table2_A Novel Strategy for Decoding and Validating the Combination Principles of Huanglian Jiedu Decoction From Multi-Scale Perspective.docx by Ke-Xin Wang (8890475)

table2_A Novel Strategy for Decoding and Validating the Combination Principles of Huanglian Jiedu Decoction From Multi-Scale Perspective.docx by Ke-Xin Wang (8890475)

table1_A Novel Strategy for Decoding and Validating the Combination Principles of Huanglian Jiedu Decoction From Multi-Scale Perspective.xlsx by Ke-Xin Wang (8890475)

Bayesian Function-on-Scalars Regression for High-Dimensional Data by Daniel R. Kowal (2592922)

“…We incorporate shrinkage priors that effectively remove unimportant scalar covariates from the model and reduce sensitivity to the number of (unknown) basis functions. …”

Changes in Punishment Function. by Huiyue Xu (12022388)

Global Aridity Index and Potential Evapotranspiration (ET0) Database: Version 3.1 by Robert Zomer (12796235)

The benchmark functions. by Shuhui Yu (1535500)

Training algorithm flow. by Xing Chen (140292)

“…<div><p>In daily life, two common algorithms are used for collecting medical disease data: data integration of medical institutions and questionnaires. …”

GELU function. by Yuanzhou Zheng (15195249)