DeepProg
Survival Analytics Framework
An open-source deep learning framework for integrating multi-omics data with survival analysis.
Deep-Learning framework for multi-omic and survival data integration
77 stars
4 watching
21 forks
Language: Python
last commit: about 1 year ago
Linked from 1 awesome list
Related projects:
Repository | Description | Stars |
---|---|---|
| A Python module for Deep Learning experiments on Audio and Text data combining classification, recommendation, and matrix factorization techniques. | 101 |
| A collection of modular deep learning components that can be easily configured and reused in various applications. | 276 |
| This repository provides an implementation of federated survival analysis using a deep learning framework. | 0 |
| A Python library for building and training neural networks using GPU acceleration | 1,169 |
| An ML framework for accelerating research and its integration into production workflows | 264 |
| An abstraction layer between various deep learning frameworks and your program. | 149 |
| A framework for accelerating PyTorch deep learning training | 876 |
| A pre-trained language model designed for various NLP tasks, including dialogue generation, code completion, and retrieval. | 94 |
| A comprehensive framework for deep reinforcement learning using PyTorch. | 1,256 |
| An open-source framework for adapting representation models to various tasks and industries | 1,743 |
| A framework for hosting and training machine learning models on a blockchain, enabling secure sharing and prediction without requiring users to pay for data or model updates. | 559 |
| An autoML framework for building and analyzing trading systems and sports betting models using machine learning algorithms. | 1,170 |
| A framework for building and training distributed deep learning models in Kubernetes environments. | 735 |
| A deep learning framework built on top of Theano, providing a wide range of models and training techniques for research and development. | 267 |
| A deep learning framework that enables efficient and flexible distributed/mobile deep learning with dynamic dataflow dependency scheduling | 28 |