Deep Learning and Aging
- 11 followers
- Harvard Medical School
- https://www.multidimensionality-of-aging.net/
Popular repositories Loading
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Images-preprocessing
Images-preprocessing PublicPreprocessing of the images for the images-based models (e.g X-ray images).
Jupyter Notebook 3
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Main-pipeline-and-Images-based-models-pipeline
Main-pipeline-and-Images-based-models-pipeline PublicCore pipeline of the project. Build the models on medical images, merge all the other models and perform the analyses.
Python
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Main-pipeline-and-Images-based-models-pipeline_static
Main-pipeline-and-Images-based-models-pipeline_static PublicCore pipeline of the project. Build the models on medical images, merge all the other models and perform the analyses.
Python
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Scalars-based-models-and-XWAS-pipeline
Scalars-based-models-and-XWAS-pipeline PublicModels based on scalar datasets (e.g blood samples). Pipeline to identify non-genetic factors associated with accelerated aging (e.g environmental variables).)
Python
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Time-series-pipeline
Time-series-pipeline PublicModels built on time series (pulse wave records, electrocardiograms, wrist accelerometer records).
Python
Repositories
- Main-pipeline-and-Images-based-models-pipeline Public
Core pipeline of the project. Build the models on medical images, merge all the other models and perform the analyses.
Deep-Learning-and-Aging/Main-pipeline-and-Images-based-models-pipeline’s past year of commit activity - PheWAS-and-GWAS-Diabetes-Correlation-pipeline Public
PheWAS pipeline for finding associations between age predictors and other laboratory phenotypes and GWAS-Diabetes pipeline for exploring correlation of age predictors with diabetes-related risk phenotypes
Deep-Learning-and-Aging/PheWAS-and-GWAS-Diabetes-Correlation-pipeline’s past year of commit activity - Time-series-pipeline Public
Models built on time series (pulse wave records, electrocardiograms, wrist accelerometer records).
Deep-Learning-and-Aging/Time-series-pipeline’s past year of commit activity - Images-preprocessing Public
Preprocessing of the images for the images-based models (e.g X-ray images).
Deep-Learning-and-Aging/Images-preprocessing’s past year of commit activity - Scalars-based-models-and-XWAS-pipeline Public
Models based on scalar datasets (e.g blood samples). Pipeline to identify non-genetic factors associated with accelerated aging (e.g environmental variables).)
Deep-Learning-and-Aging/Scalars-based-models-and-XWAS-pipeline’s past year of commit activity - Main-pipeline-and-Images-based-models-pipeline_static Public
Core pipeline of the project. Build the models on medical images, merge all the other models and perform the analyses.
Deep-Learning-and-Aging/Main-pipeline-and-Images-based-models-pipeline_static’s past year of commit activity
