论文笔记(Learning functional properties of proteins with language models)
Published:
蛋白质数据的表征，是解决当前生物医学问题的一大关键，比较好的方法是从优秀的语言模型汲取灵感，以构建类似的生信模型，这些模型最近已经在序列-结构-功能关系提取上获得了很大的成功。这篇论文对蛋白质表征学习做了深入的调研，分类并解释每一种方法，依次在如下预测目标中对他们进行评估：
Posts by Tags
Functional property learning
Protein Language Model
论文笔记(Learning functional properties of proteins with language models)
Published:
蛋白质数据的表征，是解决当前生物医学问题的一大关键，比较好的方法是从优秀的语言模型汲取灵感，以构建类似的生信模型，这些模型最近已经在序列-结构-功能关系提取上获得了很大的成功。这篇论文对蛋白质表征学习做了深入的调研，分类并解释每一种方法，依次在如下预测目标中对他们进行评估：
convolutional neural network
Geometric Vector Perceptrons
Published:
The geometric vector perceptron is a simple module for learning vector-valued and scalar-valued functions over geometric vectors and scalars. GVP-GNN can be applied to any problem where the input domain is a structure of a single macromolecule or of molecules bound to one another.
graph neural network
Geometric Vector Perceptrons
Published:
The geometric vector perceptron is a simple module for learning vector-valued and scalar-valued functions over geometric vectors and scalars. GVP-GNN can be applied to any problem where the input domain is a structure of a single macromolecule or of molecules bound to one another.
inverse folding problem
Computational Protein Design Overview
Published:
Computational Protein Design(CPD) has produced impressive results for engineering new proteins, resulting in a wide variety of applications. In this blog, the whole pipeline and mainstream methods of CPD will be concluded.
protein design
Computational Protein Design Overview
Published:
Computational Protein Design(CPD) has produced impressive results for engineering new proteins, resulting in a wide variety of applications. In this blog, the whole pipeline and mainstream methods of CPD will be concluded.
protein language models
Computational Protein Design Overview
Published:
Computational Protein Design(CPD) has produced impressive results for engineering new proteins, resulting in a wide variety of applications. In this blog, the whole pipeline and mainstream methods of CPD will be concluded.
structure modeling
Geometric Vector Perceptrons
Published:
The geometric vector perceptron is a simple module for learning vector-valued and scalar-valued functions over geometric vectors and scalars. GVP-GNN can be applied to any problem where the input domain is a structure of a single macromolecule or of molecules bound to one another.