Optimal radiomics feature of CT. حسب Jiangfa Li (18345033)

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Extracted CT and MRI images and outlined ROI. حسب Jiangfa Li (18345033)

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Statistically relevant information. حسب Jiangfa Li (18345033)

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Cross-validation of Lasso regression of enhanced MRI images. حسب Jiangfa Li (18345033)

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Univariate and multivariate results of MVI-positive and MVI-negative in training group. حسب Jiangfa Li (18345033)

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The cross-validation diagram of Lasso regression of enhanced CT combined with MRI images. حسب Jiangfa Li (18345033)

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Cross-validation of Lasso regression of enhanced CT images. حسب Jiangfa Li (18345033)

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Comparison of general clinical data between training group and verification group. حسب Jiangfa Li (18345033)

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Optimal radiomics features of CT combined with MRI. حسب Jiangfa Li (18345033)

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Analysis Codes for "Gene Signatures Predict Metastatic Organotropism in Breast Cancer: A Machine Learning Analysis of Single-Cell Data" حسب Ardo Sanjaya (18321300)

"…<p dir="ltr">This repository contains the complete set of R and Python scripts developed for the study <i>"Gene Signatures Predict Metastatic Organotropism in Breast Cancer: A Machine Learning Analysis of Single-Cell Data."…"

Flow diagram detailing the number of radiographs by data source and abnormality status from before exclusion for age (All Ages), after exclusion for age under 15 years, and after e... حسب Scott H. Lee (7430396)

OVEP code حسب Yuanfang Guan (22258765)

"…</p><p><br></p><p><br></p><p>---</p><p><br></p><p dir="ltr">## Directory structure (top-level)</p><p><br></p><p dir="ltr">- `rate_code/` </p><p>---</p><p><br></p><p dir="ltr">## Key scripts </p><p dir="ltr">- `rate_code/post_analysis_maskmissing/category_annotation/calculate_predicted_snp_by_category_maskfunctional.py` </p><p dir="ltr"> Script related to: calculate predicted snp by category maskfunctional.py</p><p dir="ltr">- `rate_code/revision/JARVIS/modules/jarvis/deep_learn_raw_seq/for_prediction_train_nn_model.py` </p><p dir="ltr"> Arg 'input_features' may take 1 of 3 possible values: - stuctured: using only structured features as input - sequence: using only sequence features as input - both: using both structured and sequence features as inputs</p><p dir="ltr">- `rate_code/revision/JARVIS/modules/jarvis/variant_classification/plot_gb_feature_importance.py` </p><p dir="ltr"> Script related to: plot gb feature importance.py</p><p dir="ltr">- `rate_code/external_test/calculate_AUC_dataset1_masksnp.py` </p><p dir="ltr"> Script related to: calculate AUC dataset1 masksnp.py</p><p dir="ltr">- `rate_code/post_analysis_maskmissing/category_annotation_calibration/calculate_predicted_snp_by_category.py` </p><p dir="ltr"> Script related to: calculate predicted snp by category.py</p><p dir="ltr">- `rate_code/post_analysis_maskmissing/category_annotation_calibration_masklabel/calculate_predicted_snp_by_category_new.py` </p><p dir="ltr"> Script related to: calculate predicted snp by category new.py</p><p dir="ltr">- `rate_code/post_analysis_maskmissing/category_annotation_calibration_masklabel/calculate_predicted_snp_by_category.py` </p><p dir="ltr"> Script related to: calculate predicted snp by category.py</p><p dir="ltr">- `rate_code/post_analysis_maskmissing/category_annotation_calibration_maskfunctional_maskrepeat/calculate_predicted_snp_by_category.py` </p><p dir="ltr"> Script related to: calculate predicted snp by category.py</p><p dir="ltr">- `rate_code/post_analysis_maskmissing/category_annotation/calculate_predicted_snp_by_category.py` </p><p dir="ltr"> Script related to: calculate predicted snp by category.py</p><p dir="ltr">- `rate_code/post_analysis_maskmissing/category_annotation/calculate_predicted_snp_by_category_nolabel.py` </p><p dir="ltr"> Script related to: calculate predicted snp by category nolabel.py</p><p dir="ltr">- `rate_code/post_analysis_maskmissing/exon_analysis_masklabel/calculate_predicted_snp_by_exon_mask.py` </p><p dir="ltr"> Script related to: calculate predicted snp by exon mask.py</p><p dir="ltr">- `rate_code/post_analysis_maskmissing/category_annotation_calibration_maskfunctional/calculate_predicted_snp_by_category.py` </p><p dir="ltr"> Script related to: calculate predicted snp by category.py</p><p dir="ltr">- `rate_code/post_analysis_maskmissing/exon_analysis/calculate_predicted_snp_by_exon_mask.py` </p><p dir="ltr"> Script related to: calculate predicted snp by exon mask.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_nonindelonlyintest_masklabel/predict_nucleotide_plot.py` </p><p dir="ltr"> Script related to: predict nucleotide plot.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_nonindelonlyintest_masklabel/predict_train.py` </p><p dir="ltr"> Script related to: predict train.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_indelonlyintest_masklabel/predict_nucleotide_plot.py` </p><p dir="ltr"> Script related to: predict nucleotide plot.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_indelonlyintest_masklabel/predict_train.py` </p><p dir="ltr"> Script related to: predict train.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/plot/plot_indel_snp_auc.py` </p><p dir="ltr"> !…"

Fitting a feast/famine cycle to a glycolysis model. حسب Paul van Lent (16876977)

Overview of the implemented simulation tool and training framework in Diffrax. حسب Paul van Lent (16876977)

Main parameters of braking system. حسب Honglei Pang (22693724)

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Simulation Results Plot of the SBW System Response: (a) Step response curve of the SBW system; (b) Sine response curve of the SBW system; (c) Steady state error curve of the SBW sy... حسب Honglei Pang (22693724)

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EMB and SBW system structure. حسب Honglei Pang (22693724)

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Raw data. حسب Honglei Pang (22693724)

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Braking performance and lateral stability on low-adhesion road surfaces: (a)Vehicle velocity; (b) Braking distance; (c) Lateral displacement; (d) Lateral acceleration; (e)Vehicle s... حسب Honglei Pang (22693724)

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Reward function related parameters. حسب Honglei Pang (22693724)

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