nci-doe-collaboration-capability
There are 36 repositories under nci-doe-collaboration-capability topic.
ATOMScience-org/AMPL
The ATOM Modeling PipeLine (AMPL) is an open-source, modular, extensible software pipeline for building and sharing models to advance in silico drug discovery.
ECP-CANDLE/Benchmarks
ECP-CANDLE Benchmarks
CBIIT/NCI-DOE-Collab-Pilot2-MuMMI
Multiscale Machine-learned Modeling Infrastructure (MuMMI) is a methodology that the Pilot 2 team has developed to study the interaction of active KRAS with the plasma membrane (and related phenomena) on very large temporal and spatial scales.
ECP-CANDLE/Candle
ECP-CANDLE level documentation
CBIIT/NCI-DOE-Collab-Pilot1-Image-Generator-for-Tabular-Data
Image Generator for Tabular Data (IGTD): Converting Tabular Data to Images for Deep Learning Using Convolutional Neural Networks
CBIIT/TULIP
Classifying RNA-seq samples into different tumor types.
CBIIT/CTULIP
Modified version for TULIP to classify Canine RNA-seq samples into different tumor types.
CBIIT/NCI-DOE-Collab-Pilot1-Single_Drug_Response
Using the Random Forest machine learning algorithm to predict the concentration, cell line- and drug-dependent response function.
CBIIT/NCI-DOE-Collab-Pilot2-DynIm
Dynamic Importance Sampling
CBIIT/NCI-DOE-Collab-Pilot3-Multitask-Convolutional-Neural-Network
MT-CNN is a CNN for Natural Language Processing and Information Extraction from free-form texts. BSEC group designed the model for information extraction from cancer pathology reports.
CBIIT/NCI-DOE-Collab-Pilot2-Autoencoder_MD_Simulation_Data
CBIIT copy of the CANDLE P2B1 benchmark code from https://github.com/ECP-CANDLE/Benchmarks/tree/develop/Pilot2/P2B1, which uses an autoencoder to generate a compressed representation of molecular dynamics simulation data
CBIIT/NCI-DOE-Collab-Pilot1-Enhanced-COXEN
Enhanced Co-Expression Extrapolation (COXEN) Gene Selection Method for Building Drug Response Prediction Models
CBIIT/NCI-DOE-Collab-Pilot1-Learning-Curve
Learning curves
CBIIT/NCI-DOE-Collab-Pilot1-Synergy_Drug_Response
Predicting drug-pair synergy from the predicted synergy probabilities of individual drugs.
CBIIT/NCI-DOE-Collab-Pilot1-Tumor_Classifier-hardening
Deep-Learning (Convolutional Neural Network) modeling procedure to classify Cancer/tumor Sites/types.
CBIIT/NCI-DOE-Collab-Pilot1-Unified-Drug-Response-Predictor-Multi-tasking
The Pilot 1 Multi-tasking Unified Drug Response Predictor benchmark, shows how to train and use a neural network model to predict tumor dose response across multiple data sources implementing UNO in PyTorch.
CBIIT/NCI-DOE-Collab-Pilot2-MemSurfer-orig
MemSurfer is a tool to compute and analyze membrane surfaces found in a wide variety of large-scale molecular simulations. MemSurfer works independent of the type of simulation, directly on the 3D point coordinates.
CBIIT/NCI-DOE-Collab-Pilot3-Active_Learning_Framework_for_NLP
Active Learning framework for Natural Language Processing of pathology reports.
CBIIT/NCI-DOE-Collab-Pilot3-FrESCO-Framework-for-Exploring-Scalable-Computational-Oncology
A modular deep-learning natural language processing (NLP) library for extracting pathology information from clinical text documents
CBIIT/NCI-DOE-Collab-Pilot3-LSTM-based-Clinical-Text-Generator
Builds LSTM-based model to generate synthetic biomedical text of desired clinical context
CBIIT/NCI-DOE-Collab-Pilot3-Multitask-DNN-NLP-Extraction
Builds a multitask DNN model for information extraction from unstructured texts
CBIIT/NCI-DOE-Collab-Pilot3-Pathology-Reports-HAN
Hierarchical attention networks for information extraction from cancer pathology reports.
CBIIT/NCI-DOE-Collab-Pilot3-SYNDATA
Suite of statistical/machine learning methods to generate discrete/categorical synthetic data