Visualizing DNA reaction trajectories with deep graph embedding approaches
원문 초록
Synthetic biologists and molecular programmers design novel nucleic acid reactions, with many potential applications. Good visualization tools are needed to help domain experts make sense of the complex outputs of folding pathway simulations of such reactions. Here we present ViDa, a new approach for visualizing DNA reaction folding trajectories over the energy landscape of secondary structures. We integrate a deep graph embedding model with common dimensionality reduction approaches, to map high-dimensional data onto 2D Euclidean space. We assess ViDa on two well-studied and contrasting DNA hybridization reactions. Our preliminary results suggest that ViDa's visualization successfully separates trajectories with different folding mechanisms, thereby providing useful insight to users, and is a big improvement over the current state-of-the-art in DNA kinetics visualization.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Finsler Multi-Dimensional Scaling: Manifold Learning for Asymmetric Dimensionality Reduction and Embedding
Active Learning for Manifold Gaussian Process Regression
Distributed Lyapunov Functions for Nonlinear Networks
Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective
Enabling stratified sampling in high dimensions via nonlinear dimensionality reduction
ETT-CKGE: Efficient Task-driven Tokens for Continual Knowledge Graph Embedding
토론
이 논문과 연결된 포럼 글이 아직 없습니다.