Paper Reading AI Learner

DeepProtein: Deep Learning Library and Benchmark for Protein Sequence Learning

2024-10-02 20:42:32
Jiaqing Xie, Yue Zhao, Tianfan Fu

Abstract

In recent years, deep learning has revolutionized the field of protein science, enabling advancements in predicting protein properties, structural folding and interactions. This paper presents DeepProtein, a comprehensive and user-friendly deep learning library specifically designed for protein-related tasks. DeepProtein integrates a couple of state-of-the-art neural network architectures, which include convolutional neural network (CNN), recurrent neural network (RNN), transformer, graph neural network (GNN), and graph transformer (GT). It provides user-friendly interfaces, facilitating domain researchers in applying deep learning techniques to protein data. Also, we curate a benchmark that evaluates these neural architectures on a variety of protein tasks, including protein function prediction, protein localization prediction, and protein-protein interaction prediction, showcasing its superior performance and scalability. Additionally, we provide detailed documentation and tutorials to promote accessibility and encourage reproducible research. This library is extended from a well-known drug discovery library, DeepPurpose and publicly available at this https URL.

Abstract (translated)

近年来,深度学习已经彻底颠覆了蛋白质科学领域,使预测蛋白质属性、结构折叠和相互作用取得了进展。本文介绍了DeepProtein,一个专门为蛋白质相关任务设计的全面且易用的深度学习库。DeepProtein整合了几个最先进的神经网络架构,包括卷积神经网络(CNN)、循环神经网络(RNN)、Transformer、图神经网络(GNN)和图Transformer(GT)。它提供了易用的界面,使领域研究人员能够将深度学习技术应用于蛋白质数据。我们还策划了一个基准,评估这些神经网络架构在包括蛋白质功能预测、蛋白质定位预测和蛋白质-蛋白质相互作用预测在内的各种蛋白质任务上的性能,展示了其在优越性能和可扩展性方面的优势。此外,我们还提供了详细的文档和教程,以促进其易用性和鼓励可重复的研究。这个库从著名的药物发现库DeepPurpose延伸,并公开发布在https://这个网址。

URL

https://arxiv.org/abs/2410.02023

PDF

https://arxiv.org/pdf/2410.02023.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model LLM Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Robot Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Time_Series Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot