scholarly journals Principal Graph and Structure Learning Based on Reversed Graph Embedding

2017 ◽  
Vol 39 (11) ◽  
pp. 2227-2241 ◽  
Author(s):  
Qi Mao ◽  
Li Wang ◽  
Ivor W. Tsang ◽  
Yijun Sun
Keyword(s):  
Structure Learning ◽  
Graph Embedding ◽  
Principal Graph

scholarly journals Reversed graph embedding resolves complex single-cell developmental trajectories

2017 ◽  
Author(s):  
Xiaojie Qiu ◽  
Qi Mao ◽  
Ying Tang ◽  
Li Wang ◽  
Raghav Chawla ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Developmental Trajectories ◽  
Single Cells ◽  
Graph Embedding ◽  
Developmental Trajectory ◽  
Loss Of Function ◽  
Cell Fate Decisions ◽  
Principal Graph ◽  
Cell Trajectories

AbstractOrganizing single cells along a developmental trajectory has emerged as a powerful tool for understanding how gene regulation governs cell fate decisions. However, learning the structure of complex single-cell trajectories with two or more branches remains a challenging computational problem. We present Monocle 2, which uses reversed graph embedding to reconstruct single-cell trajectories in a fully unsupervised manner. Monocle 2 learns an explicit principal graph to describe the data, greatly improving the robustness and accuracy of its trajectories compared to other algorithms. Monocle 2 uncovered a new, alternative cell fate in what we previously reported to be a linear trajectory for differentiating myoblasts. We also reconstruct branched trajectories for two studies of blood development, and show that loss of function mutations in key lineage transcription factors diverts cells to alternative branches on the a trajectory. Monocle 2 is thus a powerful tool for analyzing cell fate decisions with single-cell genomics.

scholarly journals Graph Embedding Method Based Genetical Trajectory Reveals Migration History Among East Asians

2019 ◽  
Author(s):  
Zhuang Wei ◽  
Ching-Wen Chang ◽  
Van Luo ◽  
Beilei Bian ◽  
Xuewei Ding
Keyword(s):  
Population Genetics ◽  
Human Population ◽  
Structure Learning ◽  
Neurological Diseases ◽  
Enrichment Analysis ◽  
Graph Embedding ◽  
Differential Analysis ◽  
Human Population Genetics ◽  
Embedding Method ◽  
Asian Populations

ABSTRACTAn important issue in human population genetics is the ancestry. By extracting the ancestral information retained in the single nucleotide polymorphism (SNP) of genomic DNA, the history of migration and reproduction of the population can be reconstructed. Since the SNP data of population are multidimensional, their dimensionality reduction can demonstrate their potential internal connections. In this study, the graph and structure learning based Graph Embedding method commonly used in single cell mRNA sequencing was applied to human population genetics research to decrease the data dimension. As a result, the human population trajectory of East Asia based on 1000 Genomes Project was reconstructed to discover the inseparable relationship between the Chinese population and other East Asian populations. These results are visualized from various ancestry calculators such as E11 and K12B. Finally, the unique SNPs along the psudotime of trajectory were found by differential analysis. Bioprocess enrichment analysis was also used to reveal that the genes of these SNPs may be related to neurological diseases. These results will lay the data foundation for precision medicine.

A Structure Learning Method for Block-Based Neural Networks

2013 ◽  
Vol 133 (10) ◽  
pp. 1976-1982 ◽  
Author(s):  
Hidetaka Watanabe ◽  
Seiichi Koakutsu ◽  
Takashi Okamoto ◽  
Hironori Hirata
Keyword(s):  
Neural Networks ◽  
Structure Learning ◽  
Learning Method ◽  
Block Based

A Transition-Based Knowledge Graph Embedding with Adapting New Entities

2016 ◽  
Author(s):  
A-Yeong Kim ◽  
◽  
Hee-Guen Yoon ◽  
Seong-Bae Park ◽  
Se-Young Park ◽  
...  
Keyword(s):  
Graph Embedding ◽  
Knowledge Graph

scholarly journals Robust Structure Learning of Bayesian Network by Identifying Significant Dependencies

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 116661-116675 ◽  
Author(s):  
Yuguang Long ◽  
Limin Wang ◽  
Zhiyi Duan ◽  
Minghui Sun
Keyword(s):  
Bayesian Network ◽  
Structure Learning

scholarly journals Causal imprinting in causal structure learning

2012 ◽  
Vol 65 (3) ◽  
pp. 381-413 ◽  
Author(s):  
Eric G. Taylor ◽  
Woo-kyoung Ahn
Keyword(s):  
Structure Learning ◽  
Causal Structure

scholarly journals Graph Learning for Combinatorial Optimization: A Survey of State-of-the-Art

2021 ◽  
Author(s):  
Yun Peng ◽  
Byron Choi ◽  
Jianliang Xu
Keyword(s):  
Machine Learning ◽  
Combinatorial Optimization ◽  
Graph Embedding ◽  
Partial Solution ◽  
Complex Data ◽  
Learning Methods ◽  
Graph Learning ◽  
Second Stage ◽  
End To End ◽  
Embedding Methods

AbstractGraphs have been widely used to represent complex data in many applications, such as e-commerce, social networks, and bioinformatics. Efficient and effective analysis of graph data is important for graph-based applications. However, most graph analysis tasks are combinatorial optimization (CO) problems, which are NP-hard. Recent studies have focused a lot on the potential of using machine learning (ML) to solve graph-based CO problems. Most recent methods follow the two-stage framework. The first stage is graph representation learning, which embeds the graphs into low-dimension vectors. The second stage uses machine learning to solve the CO problems using the embeddings of the graphs learned in the first stage. The works for the first stage can be classified into two categories, graph embedding methods and end-to-end learning methods. For graph embedding methods, the learning of the the embeddings of the graphs has its own objective, which may not rely on the CO problems to be solved. The CO problems are solved by independent downstream tasks. For end-to-end learning methods, the learning of the embeddings of the graphs does not have its own objective and is an intermediate step of the learning procedure of solving the CO problems. The works for the second stage can also be classified into two categories, non-autoregressive methods and autoregressive methods. Non-autoregressive methods predict a solution for a CO problem in one shot. A non-autoregressive method predicts a matrix that denotes the probability of each node/edge being a part of a solution of the CO problem. The solution can be computed from the matrix using search heuristics such as beam search. Autoregressive methods iteratively extend a partial solution step by step. At each step, an autoregressive method predicts a node/edge conditioned to current partial solution, which is used to its extension. In this survey, we provide a thorough overview of recent studies of the graph learning-based CO methods. The survey ends with several remarks on future research directions.

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