Image 2_Multi-omics and single-cell approaches reveal molecular subtypes and key cell interactions in hepatocellular carcinoma.jpeg حسب Xueqing Zou (2144722)

"…</p>Methods<p>In this study, we applied ten multi-omics classification algorithms to identify three distinct molecular subtypes of HCC (C1–C3). …"

Image 3_Multi-omics and single-cell approaches reveal molecular subtypes and key cell interactions in hepatocellular carcinoma.jpeg حسب Xueqing Zou (2144722)

"…</p>Methods<p>In this study, we applied ten multi-omics classification algorithms to identify three distinct molecular subtypes of HCC (C1–C3). …"

Table 3_Multi-omics and single-cell approaches reveal molecular subtypes and key cell interactions in hepatocellular carcinoma.xlsx حسب Xueqing Zou (2144722)

"…</p>Methods<p>In this study, we applied ten multi-omics classification algorithms to identify three distinct molecular subtypes of HCC (C1–C3). …"

Data Sheet 1_Exploring the molecular mechanisms of phthalates in the comorbidity of preeclampsia and depression by integrating multiple datasets.zip حسب Xinpeng Tian (646275)

"…Machine learning algorithms were applied to select core diagnostic genes, followed by validation in independent cohorts. …"

Image 1_Multi-omics and single-cell approaches reveal molecular subtypes and key cell interactions in hepatocellular carcinoma.jpeg حسب Xueqing Zou (2144722)

"…</p>Methods<p>In this study, we applied ten multi-omics classification algorithms to identify three distinct molecular subtypes of HCC (C1–C3). …"

Image 6_Multi-omics and single-cell approaches reveal molecular subtypes and key cell interactions in hepatocellular carcinoma.jpeg حسب Xueqing Zou (2144722)

"…</p>Methods<p>In this study, we applied ten multi-omics classification algorithms to identify three distinct molecular subtypes of HCC (C1–C3). …"

Stochastic-SplitGAS: A Quantum Monte Carlo Multi-Reference Perturbation Theory Based on the Imaginary-Time Evolution of Effective Hamiltonians حسب Luca Bonfirraro (22773818)

"…The generalized active space algorithm allows the flexible partitioning of the configurational space. …"

Image 1_Characterization of cancer-related fibroblasts in bladder cancer and construction of CAFs-based bladder cancer classification: insights from single-cell and multi-omics ana... حسب Zhaokai Zhou (15239078)

"…Next, we comprehensively explored the distinct heterogeneity and characteristics for four CAFs-based BLCA subtypes. Moreover, machine learning algorithms were applied to identify novel potential targets for each subtype, and experimentally validate their effects.…"

Table 1_Characterization of cancer-related fibroblasts in bladder cancer and construction of CAFs-based bladder cancer classification: insights from single-cell and multi-omics ana... حسب Zhaokai Zhou (15239078)

"…Next, we comprehensively explored the distinct heterogeneity and characteristics for four CAFs-based BLCA subtypes. Moreover, machine learning algorithms were applied to identify novel potential targets for each subtype, and experimentally validate their effects.…"

Image 2_Characterization of cancer-related fibroblasts in bladder cancer and construction of CAFs-based bladder cancer classification: insights from single-cell and multi-omics ana... حسب Zhaokai Zhou (15239078)

"…Next, we comprehensively explored the distinct heterogeneity and characteristics for four CAFs-based BLCA subtypes. Moreover, machine learning algorithms were applied to identify novel potential targets for each subtype, and experimentally validate their effects.…"

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. …"

Data Sheet 1_Development and validation of a disulfidptosis-related genes signature for predicting outcomes and immunotherapy in acute myeloid leukemia.docx حسب Han Gong (4764102)

"…Least absolute shrinkage and selection operator (LASSO) Cox model was used to generate a disulfidptosis-related signature. …"

Table 1_Integrative multi-omics analysis identifies a PTM-related immune signature and IRF9 as a driver in ccRCC.docx حسب Zixiang Li (7014416)

"…</p>Methods<p>We intersected immune-related genes, PTM-related genes, and differentially expressed genes in TCGA-KIRC to derive candidates and built a prognostic model across TCGA and E-MTAB-1980 using multiple algorithms, selecting a random survival forest-based post-translational modification-related signature (PTMRS) with the best performance. …"

Supplementary file 1_Integrative multi-omics analysis identifies a PTM-related immune signature and IRF9 as a driver in ccRCC.docx حسب Zixiang Li (7014416)

"…</p>Methods<p>We intersected immune-related genes, PTM-related genes, and differentially expressed genes in TCGA-KIRC to derive candidates and built a prognostic model across TCGA and E-MTAB-1980 using multiple algorithms, selecting a random survival forest-based post-translational modification-related signature (PTMRS) with the best performance. …"

Table 2_Integrative multi-omics analysis identifies a PTM-related immune signature and IRF9 as a driver in ccRCC.docx حسب Zixiang Li (7014416)

"…</p>Methods<p>We intersected immune-related genes, PTM-related genes, and differentially expressed genes in TCGA-KIRC to derive candidates and built a prognostic model across TCGA and E-MTAB-1980 using multiple algorithms, selecting a random survival forest-based post-translational modification-related signature (PTMRS) with the best performance. …"

Data Availability for Barrier Island Response to Energetic Storms: a Global View حسب Valeria Fanti (14857549)

"…As wave direction is a circular variable, in order to allow its use in correlation analysis it was linearized with the sine function and referenced to 270°. …"

A large open access dataset of transillumination imaging toward realization the optical computed tomography حسب To Ni Phan Van (20072530)

"…This study aimed to overcome this obstacle by introducing a comprehensive dataset of transillumination images. Methods and algorithms for generating depth-dependent point-spread function and transillumination images were presented. …"

Particle swarm optimization method for energy management of the hybrid system of an electric vehicle charging station حسب Olukorede Adenuga (20139099)

"…The developed PSO methods and algorithms can be useful for the resolution of numerous energy management problems in smart grid applications, provincial and national control centers, and research and educational institutions.…"

Table 1_Exploring the role of TikTok for intersectionality marginalized groups: the case of Muslim female content creators in Germany.docx حسب Fatima El Sayed (20146977)

"…They shape the platform’s functionalities through creative use, while TikTok’s algorithm and virality logic drive creators to blend entertainment with personal content. …"

spine_quantification_all_images.xlsx حسب Ana M Jiménez-García (20396996)

"…It includes a number of neuropsychiatric disturbances including impaired motor activity and coordination, intellectual and cognitive function.</p><p dir="ltr">Results: In the present study, we used a rat early-stage HE model by triple portal vein ligation for 50 days To gain a better understanding of the effect of HE on the brain, artificial intelligence algorithms based on convolutional neuronal networks were implemented for the unbiased quantification of the brain images which were stained by Golgi-Cox immunohistochemistry. …"