The speciation of NANEAT model evolution. حسب Wenbing Shi (5806160)

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Code حسب Baoqiang Chen (21099509)

SpeLL: An Agent for Natural Language-Driven Intelligent Spectral Modeling حسب Jiashun Fu (20888176)

"…The core strength of SpeLL lies in its dual RAG pathways. The Code RAG provides specialized code knowledge for spectral data analysis, enabling the LLM to generate robust and domain-specific analytical scripts that address the implementation and optimization of algorithms. …"

Elementary effects (EE) results for chorionic vessel generation employing the input parameter ranges from Table 1. حسب Diana C. de Oliveira (9220729)

data and code حسب PENGCHENG LIU (20680641)

<b>Correction of the Code, Data and Figure files for Figure 3 of "Quantum-enhanced simulated annealing using Rydberg atoms"</b> حسب Seokho Jeong (20596511)

Elementary effects (EE) results for the generation of villous vessels within a placentone employing the input parameter ranges from Table 1. حسب Diana C. de Oliveira (9220729)

Flowchart for GR codes. حسب R. Sakthivel (2589547)

"…The algorithm was developed and coded in Verilog and simulated using Modelsim. …"

<b>Algorithm Pseudocode</b> حسب Yibin Zhao (22425801)

Mechanomics Code - JVT حسب Carlo Vittorio Cannistraci (5854046)

"…At the beginning of the code, there is a help section that explains how to use it.…"

AUC values for each graph <i>G</i> and pair of random walks <i>w</i>_<i>x</i> and <i>w</i>_<i>y</i>. حسب Adilson Vital Jr. (20135941)

Oresme حسب Mehmet Keçeci (14301782)

Illustration of the text transformation process before and after synonym replacement: The process begins with a sample initial text segment (Sample text before synonym replacement’... حسب Enock Niyonkuru (21246879)

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Schematic of the approach: This schematic illustrates the entire workflow of the project. حسب Enock Niyonkuru (21246879)

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Text Transformation Pipeline: An example of the multi-stage text transformation pipeline applied to a sample abstract (PMID: 30609739). حسب Enock Niyonkuru (21246879)

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Comparative analysis of WordNet replacement impact on data distribution. حسب Enock Niyonkuru (21246879)

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Comparison of window size for embedding with Wordnet synonym replacement (WN) and without (PM). While Table 1 compared the effects of different values of using a window size of 10,... حسب Enock Niyonkuru (21246879)

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Comparison of mean interconcept distance for embedding with WordNet synonym replacement (WN) and without (PM). The initial number of unique concepts in the total corpus was 3,018,9... حسب Enock Niyonkuru (21246879)

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Comparative analysis of WN and PM methodologies: Figure (a) displays the bar chart comparing WN and PM across five distinct concept sets (Methods), highlighting the number of conce... حسب Enock Niyonkuru (21246879)

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TRENDY method code files حسب 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. …"