An open-source, tool-augmented conversational language model from Fudan University
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Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology
Toolkit for Machine Learning, Natural Language Processing, and Text Generation, in TensorFlow. This is part of the CASL project: http://casl-project.ai/
A curated list of data mining papers about fraud detection.
Python package for graph neural networks in chemistry and biology
A PyTorch and TorchDrug based deep learning library for drug pair scoring. (KDD 2022)
OpenChem: Deep Learning toolkit for Computational Chemistry and Drug Design Research
Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.
Chainer Chemistry: A Library for Deep Learning in Biology and Chemistry
RNNLG is an open source benchmark toolkit for Natural Language Generation (NLG) in spoken dialogue system application domains. It is released by Tsung-Hsien (Shawn) Wen from Cambridge Dialogue Systems Group under Apache License 2.0.
Deep Learning Library for Single Cell Analysis
Tools for computational pathology
AI-based pathology predicts origins for cancers of unknown primary - Nature
PyTorch Geometric Signed Directed is a signed/directed graph neural network extension library for PyTorch Geometric. The paper is accepted by LoG 2023.
Axon/Myelin segmentation using Deep Learning
RootPainter: Deep Learning Segmentation of Biological Images with Corrective Annotation
Friendly Environment for Neural Networks (fenn) is a simple Python framework for building ML/DL workflows and LLM agents faster, with prebuilt trainers, agent templates, logging, configuration management, and much more.
NeoDTI: Neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions
PyTorch implementation of the MIDAS algorithm for single-cell multimodal data integration (Nature Biotechnology 2024).
3D shape analysis using deep learning
Accurate segmentation of bacterial microscope images using deep learning synthetically generated image data.