Techniques for efficiently loading graph data into memory are provided. A plurality of node ID lists are retrieved from storage. Each node ID list is ordered based on one or more order criteria, such as node ID,
通过原子连通图形的分层过程在蛋白质嵌入中捕捉不同粒度水平的分子结构,生成新的3D结构(可以看一下17年NIPs的Protein Interface Prediction using Graph Convolutional Networks)。 量化蛋白质相互作用:有很多不同的数据形式,包括化学结构,结构亲和力,物理和化学性质,氨基酸序列等来提高蛋白质相互作用预测的定量。可以使用图...
Graph Representation is defined as the way of representing a graph using a compressed adjacency list format. In this format, the vertices of the graph are stored in an array and the edges of all vertices are packed into another array. The weights of the edges are stored in a parallel array...
NetflixGraph is a compact in-memory data structure used to represent directed graph data. You can use NetflixGraph to vastly reduce the size of your application’s memory footprint, potentially by an order of magnitude or more. If your application is I/O bound, you may be able to remove...
Built from a compacted de Bruijn graph Static, once constructed, the indexed kmer set cannot be modified Fast and memory-efficient even for the most extensive kmer set Graph construction To be constructed, a Blight index needs a Fasta file, whose sequence contains the kmer to index with no du...
In graph representation learning, learned vector representations (or embeddings) of graph elements are generated such that they capture the structure and semantics of the network along with any downstream supervised task (Fig.1). There is a wide range of methods for graph representation learning, in...
In recent years, there has been a surge of research interest in utilizing neural networks to handle graph-structured data. Among them, graph convolutional networks (GCNs) have been shown effective in graph representation learning. They can model complex attribute features and structure features of ...
InfoGraph:利用了graph和自身的node以及其他图的node构建对比学习的损失。 InfoGraph*:除了使用监督损失外,还引入双encoder的结构,并且增加一个MI的约束。 二、实验方案 作者提出了两种方案,一种是InfoGraph,侧重于图的无监督表示学习,另外一种是InfoGraph*,它是在InfoGraph基础上拓展的半监督学习方法。
We investigated whether dogs remember their spontaneous past actions relying on episodic-like memory. Dogs were trained to repeat a small set of actions upon request. Then we tested them on their ability to repeat other actions produced by themselves, including actions performed spontaneously in every...
In this section, we mainly introduce text-based PTMs since the fantastic Transformer-based PTMs for representation learning begin from NLP, leaving the introduction of the PTMs for graph in Chap.6, multi-modality in Chap.7, and knowledge in Chaps.9,10,11, and12. In the rest of this ch...