Highlights
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Christensen-Lab-Dartmouth/MethylNet
Christensen-Lab-Dartmouth/MethylNet PublicModular framework for deep learning predictions on methylation data.
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PathFlowAI
PathFlowAI PublicA High-Throughput Workflow for Preprocessing, Deep Learning Analytics and Interpretation in Digital Pathology
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InteractionTransformer
InteractionTransformer PublicExtract meaningful interactions from machine learning models to obtain machine-learning performance with statistical model interpretability.
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Christensen-Lab-Dartmouth/PyMethylProcess
Christensen-Lab-Dartmouth/PyMethylProcess PublicPreprocessing methylation pipeline, written in python. Easy to use and highly parallelized.
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PolyCRACKER-Unofficial-Mirror
PolyCRACKER-Unofficial-Mirror PublicA robust method for the unsupervised partitioning of polyploid subgenomes by signatures of repetitive DNA evolution https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-019-5828-5
Highlights
- Pro
Pinned Loading
-
Christensen-Lab-Dartmouth/MethylNet
Christensen-Lab-Dartmouth/MethylNet PublicModular framework for deep learning predictions on methylation data.
-
PathFlowAI
PathFlowAI PublicA High-Throughput Workflow for Preprocessing, Deep Learning Analytics and Interpretation in Digital Pathology
-
InteractionTransformer
InteractionTransformer PublicExtract meaningful interactions from machine learning models to obtain machine-learning performance with statistical model interpretability.
-
Christensen-Lab-Dartmouth/PyMethylProcess
Christensen-Lab-Dartmouth/PyMethylProcess PublicPreprocessing methylation pipeline, written in python. Easy to use and highly parallelized.
-
PolyCRACKER-Unofficial-Mirror
PolyCRACKER-Unofficial-Mirror PublicA robust method for the unsupervised partitioning of polyploid subgenomes by signatures of repetitive DNA evolution https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-019-5828-5
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