Corpus ID: 219966346

Graph Learning for Inverse Landscape Genetics

@article{Dharangutte2020GraphLF,
  title={Graph Learning for Inverse Landscape Genetics},
  author={Prathamesh Dharangutte and Christopher Musco},
  journal={ArXiv},
  year={2020},
  volume={abs/2006.12334}
}
  • Prathamesh Dharangutte, Christopher Musco
  • Published 2020
  • Computer Science, Mathematics
  • ArXiv
  • The problem of inferring unknown graph edges from numerical data at a graph’s nodes appears in many forms across machine learning. We study a version of this problem that arises in the field of landscape genetics, where genetic similarity between populations of organisms living in a heterogeneous landscape is explained by a weighted graph that encodes the ease of dispersal through that landscape. Our main contribution is an efficient algorithm for inverse landscape genetics, which is the task… CONTINUE READING

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 55 REFERENCES
    Circuit theory predicts gene flow in plant and animal populations
    560
    Current approaches using genetic distances produce poor estimates of landscape resistance to interindividual dispersal.
    84
    Learning Networks from Random Walk-Based Node Similarities
    2
    Sample design effects in landscape genetics
    64
    Stéphane Aulagnier
    • 2004
    Viral Shah
    • 2016
    A Constrained ℓ1 Minimization Approach to Sparse Precision Matrix Estimation
    675
    A Variational Baysian Framework for Graphical Models
    774
    An algorithm for treerealizability of distance matrices
    • 1990