Scatter plot of the first two PC for the pre- and post-ensiled data set. حسب Lorenzo Serva (6184358)

Anchor positions. حسب Caesar Tawfeeq (21087272)

Effective connectivity analysis. حسب Akke Mats Houben (21572855)

Oscillatory Field Genesis: The Emergent Architecture of Spacetime, Matter, and Memory حسب Drippy Stone (21218843)

Example of the results of the model fitting in the 103-dimensional parameter space. حسب Kevin J. Wischnewski (21354521)

"…Plot <b>(E)</b> shows the first half of the frequency parameters, belonging to the brain regions in the left hemisphere in the Schaefer atlas, and <b>(F)</b> shows the second half, which represents the right hemisphere. …"

Example recording illustrating IMU data segmentation using change-point detection. حسب Romain Fayat (22404831)

"…IMU and video recordings are synchronized and displayed at half speed.</p> <p>(MP4)</p>…"

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. …"

Presentation of the DySCo framework. حسب Giuseppe de Alteriis (20846230)

"…B: Why dFC is important: i) In this toy example, 3 brain signals are recorded, referred to 3 anatomical locations (). In the first half of the recording (blue half) and are highly correlated (high FC), while in the second half and are highly correlated. …"

<b>Force-Position-Speed Planning and Roughness rediction for Robotic Polishing</b> حسب Ma Haohao (19780875)

"…The improved dung beetle optimization algorithm, back propagation neural network, finite element analysis and response surface method provide a strong guarantee for the selection of robotic polishing process parameters. …"

Single neurons in the human substantia nigra encode social learning signals حسب Arianna Davis (21370643)

"…Scripts for carrying out neural analyses are in the neural folder. Note you will also need to download the OSORT offline sorting algorithm, which is linked as a related work here and also cited in the paper.…"

Research on Olympic medal prediction based on GA-BP and logistic regression model Extended data حسب Sanglin Zhao (20599835)

"…</p><p dir="ltr">Method:</p><p dir="ltr">This article uses the GA-BP algorithm model, combined withgenetic algorithm (GA) and backpropagationneural network (BPNN),to optimize the weightsand bias parameters of the BP neural networkusing the global search capability of genetic algorithm, thereby improvingtraining efficiency and prediction performance.By estimating the number of Olympic gold medals and total medals, verifying the accuracy of the model, and predicting the medal table forthe 2028 Los Angeles Olympics. …"

Image 1_Exploring the role of neutrophil extracellular traps in neuroblastoma: identification of molecular subtypes and prognostic implications.tif حسب Can Qi (540350)

"…Univariate Cox analysis and the LASSO algorithm were used to identify biomarkers for prognosis. …"

Table 3_Exploring the role of neutrophil extracellular traps in neuroblastoma: identification of molecular subtypes and prognostic implications.xlsx حسب Can Qi (540350)

"…Univariate Cox analysis and the LASSO algorithm were used to identify biomarkers for prognosis. …"

Table 1_Exploring the role of neutrophil extracellular traps in neuroblastoma: identification of molecular subtypes and prognostic implications.xlsx حسب Can Qi (540350)

"…Univariate Cox analysis and the LASSO algorithm were used to identify biomarkers for prognosis. …"

Table 2_Exploring the role of neutrophil extracellular traps in neuroblastoma: identification of molecular subtypes and prognostic implications.xlsx حسب Can Qi (540350)

"…Univariate Cox analysis and the LASSO algorithm were used to identify biomarkers for prognosis. …"

Table 2_Identification of key genes in membranous nephropathy and non-alcoholic fatty liver disease by bioinformatics and machine learning.xlsx حسب Jiachen Fan (21399268)

"…A protein-protein interaction (PPI) network was constructed, and key genes associated with both diseases were identified using Cytoscape software and machine learning algorithms. The correlation between immune cell infiltration and the two diseases was evaluated using the CIBERSORT algorithm. …"

Image 2_Identification of key genes in membranous nephropathy and non-alcoholic fatty liver disease by bioinformatics and machine learning.tif حسب Jiachen Fan (21399268)

"…A protein-protein interaction (PPI) network was constructed, and key genes associated with both diseases were identified using Cytoscape software and machine learning algorithms. The correlation between immune cell infiltration and the two diseases was evaluated using the CIBERSORT algorithm. …"

Table 1_A multicentric real-world observational study to describe the use and efficacy of follitropin delta for IVF/ICSI procedures in patients at risk of hypo-response.docx حسب Anne-Claire Deloire (22222351)

"…The prescribed daily dose was usually based on the approved algorithm (N = 26; 74.3%) with a mean daily dose of 14.2 ± 4.1 mcg, resulting in a mean total dose of 187.7 ± 135.6 mcg. …"

Image 1_Identification of key genes in membranous nephropathy and non-alcoholic fatty liver disease by bioinformatics and machine learning.tif حسب Jiachen Fan (21399268)

"…A protein-protein interaction (PPI) network was constructed, and key genes associated with both diseases were identified using Cytoscape software and machine learning algorithms. The correlation between immune cell infiltration and the two diseases was evaluated using the CIBERSORT algorithm. …"

Image 3_Identification of key genes in membranous nephropathy and non-alcoholic fatty liver disease by bioinformatics and machine learning.tif حسب Jiachen Fan (21399268)

"…A protein-protein interaction (PPI) network was constructed, and key genes associated with both diseases were identified using Cytoscape software and machine learning algorithms. The correlation between immune cell infiltration and the two diseases was evaluated using the CIBERSORT algorithm. …"