TigerLily: Finding drug interactions in silico with the Graph
원문 초록
Tigerlily is a TigerGraph based system designed to solve the drug interaction prediction task. In this machine learning task, we want to predict whether two drugs have an adverse interaction. Our framework allows us to solve this highly relevant real-world problem using graph mining techniques in these steps: (a) Using PyTigergraph we create a heterogeneous biological graph of drugs and proteins. (b) We calculate the personalized PageRank scores of drug nodes in the TigerGraph Cloud. (c) We embed the nodes using sparse non-negative matrix factorization of the personalized PageRank matrix. (d) Using the node embeddings we train a gradient boosting based drug interaction predictor.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Efficient Parallel Genetic Algorithm for Perturbed Substructure Optimization in Complex Network
Correlation-Aware Graph Convolutional Networks for Multi-Label Node Classification
Higher-Order GNNs Meet Efficiency: Sparse Sobolev Graph Neural Networks
Accurate and Fast Estimation of Temporal Motifs using Path Sampling
Refining Wikidata Taxonomy using Large Language Models
A Versatile Framework for Attributed Network Clustering via K-Nearest Neighbor Augmentation
토론
이 논문과 연결된 포럼 글이 아직 없습니다.