TRENDY method code files by Yue Wang (20581715)

“…<p dir="ltr">code files for TRENDY method, used for inferring gene regulatory networks (GRN) from single-cell gene expression data</p><p dir="ltr">TRENDY algorithm itself uses the following packages: numpy=1.24.3, scipy=1.13.1, sklearn=1.4.2, torch=2.2.2,</p><p dir="ltr">other algorithms and comparison files need extra packages: pingouin=0.5.3, pandas=2.2.2, xgboost=2.0.3</p><p dir="ltr">warning: the NonlinearODEs algorithm (xgbgrn.py) runs well on Windows, but might crash on MacOS</p><p dir="ltr">manuscript for TRENDY method: GRN_transformer.pdf</p><p dir="ltr">major code files:</p><p dir="ltr">TRENDY_tutorial.py: a tutorial for using TRENDY method. to apply the TRENDY method, it also needs the following files: models.py, wendy_solver.py in previous_methods folder, trendy_1.pth and trendy_2.pth in weights folder</p><p dir="ltr">models.py: contains all three versions of the TE(k) model</p><p dir="ltr">train_trendy_first_half.py: train the TE(k=1) model for the first half of TRENDY method. the trained weights trendy_1.pth is in the folder weights</p><p dir="ltr">train_trendy_second_half.py: train the TE(k=3) model for the second half of TRENDY method. the trained weights trendy_2.pth is in the folder weights</p><p dir="ltr">code files for training other models:</p><p dir="ltr">train_GENIE3_rev.py: train the GENIE3_rev method. the trained weights genie_rev.pth is in the folder weights</p><p dir="ltr">train_NonlinearODEs_rev.py: train the NonlinearODEs_rev method. the trained weights nlode_rev.pth is in the folder weights</p><p dir="ltr">train_SINCERITIES_rev.py: train the SINCERITIES_rev method. the trained weights sinc_rev.pth is in the folder weights</p><p dir="ltr">code files for comparing different methods:</p><p dir="ltr">test_SINC_new.py: used to compare different methods on SINC data</p><p dir="ltr">test_DREAM4_new.py: used to compare different methods on DREAM4 data</p><p dir="ltr">test_THP1_new.py: used to compare different methods on THP-1 data</p><p dir="ltr">test_hESC_new.py: used to compare different methods on hESC data</p><p dir="ltr">code for previously known methods, all in the folder previous_methods:</p><p dir="ltr">sincerities.py: code for SINCERITIES method</p><p dir="ltr">xgbgrn.py: code for NonlinearODEs method</p><p dir="ltr">GENIE3.py: code for GENIE3 method</p><p dir="ltr">wendy_solver.py: code for WENDY method</p><p dir="ltr">auxiliary code files:</p><p dir="ltr">methods.py: contains functions for different methods</p><p dir="ltr">evaluation.py: compare the inferred GRN with the ground truth GRN and calculate AUROC and AUPRC</p><p dir="ltr">plots_new.py: draw plots</p><p dir="ltr">nd_alg.py: code for network deconvolution method for enhancing inferred GRNs</p><p dir="ltr">brane_alg.py: code for BRANE Cut method for enhancing inferred GRNs</p><p dir="ltr">generate training data, all in the folder Data_generation:</p><p dir="ltr">A_data_generation.py generates a random GRN, generate.py generates all files in the following folder total_data_10, the other four files are previously known methods</p><p dir="ltr">data sets:</p><p dir="ltr">folder total_data_10 (<a href="https://zenodo.org/records/13929908" rel="nofollow" target="_blank">https://zenodo.org/records/13929908</a>, not uploaded here): saves the generated data for training and validation. for different endings: A is the ground truth GRN, cov is the covariance matrix, data is the original data, genie is the inferred GRN by GENIE3, nlode is the inferred GRN by NonlinearODEs, revcov is the Ktstar matrix, sinc is the inferred GRN by SINCERITIES, wendy is the inferred GRN by WENDY. these data files are generated by the files in folder Data_generation. here we only upload the files for testing. for full files of this folder, see <a href="https://zenodo.org/records/13929908" rel="nofollow" target="_blank">https://zenodo.org/records/13929908</a></p><p dir="ltr">folder rev_wendy_all_10 (not uploaded here. see <a href="https://zenodo.org/records/13929908" rel="nofollow" target="_blank">https://zenodo.org/records/13929908</a>): saves the inferred Kt' matrix (xxx_ktstar files) and A_1 matrix (xxx_revwendy files) in TRENDY</p><p dir="ltr">folder SINC: ground truth GRNs and inferred GRNs in SINC data set. …”

Mandelbrot set source codes. by Khairul Habib Alam (20521419)

Julia set source codes. by Khairul Habib Alam (20521419)

Code for "Dataset of binocularly coded steady-state visual evoked potentials recorded with an augmented reality headset" by Yufeng Ke (16539882)

“…</p><p dir="ltr">Codes under the “Code” folder are presented to validate data quality and algorithm performance, which utilize publicly available code with references noted. …”

Experimental environment configuration. by Bei Huang (485021)

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Performance evaluation results of the ablation experiment. by Bei Huang (485021)

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Comparison between Ghiasi et al.’s 3D virtual scene aesthetic style expression and Hybrid Garden StyleNet. by Bei Huang (485021)

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Structure of the style prediction network. by Bei Huang (485021)

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Structure of the style transfer network. by Bei Huang (485021)

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Comparison of network training duration. by Bei Huang (485021)

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Comparison on different number of residual blocks. by Bei Huang (485021)

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Overall flow chart of the model. by Bei Huang (485021)

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Prediction to belong to Cluster 1 or Cluster 2 using random forest algorithm. by Olivier Mascle (14164997)

Data Sheet 1_Fast forward modeling and response analysis of extra-deep azimuthal resistivity measurements in complex model.docx by Pan Zhang (206408)

“…Considering the increased detection range of EDARM and the requirements for computational efficiency, this paper presents a 2.5-dimensional (2.5D) finite element method (FEM). By leveraging the symmetry of simulated signals in the spectral domain, the algorithm reduces computation time by 50%, significantly enhancing computational efficiency while preserving accuracy. …”

USDA-ARS Tucson, Arizona 2014-2022 Data Reservoir of Field Experiments with Managed Honey Bee Colonies: Annotated Hive Frame Photos: Dataset I by Vladimir Kulyukin (21983990)

Data Sheet 1_Developing decision support algorithm for hypertension medications for use in a digital therapeutic system.pdf by Jeannie K. Lee (9026981)

Data Sheet 2_Developing decision support algorithm for hypertension medications for use in a digital therapeutic system.pdf by Jeannie K. Lee (9026981)

Example gauge chart outputs produced by the M.E.D.V.I.S. algorithm. by Hector Torres (11708207)

Data Sheet 1_Strategies for population-level identification of post-acute sequelae of COVID-19 through health administrative data.pdf by Cristina Mazzali (22090328)

“…From each included study, we extracted data on design, algorithms used for outcome identification (sources, coding systems, codes, time criteria/thresholds), and whether significant associations with SARS-CoV-2 infection were reported.…”

Study inclusion and exclusion flowchart. by Jia Wei (120974)

“…Relying solely on diagnostic codes for CAP surveillance leads to substantial under-detection; combining EHR data across multiple algorithms enhances identification accuracy. …”