SEMgraph: an R Package for Causal Network Inference of High-Throughput Data with Structural Equation Models.

@article{Grassi2021SEMgraphAR,
  title={SEMgraph: an R Package for Causal Network Inference of High-Throughput Data with Structural Equation Models.},
  author={Mario Grassi and Fernando Palluzzi and Barbara Tarantino},
  journal={Bioinformatics},
  year={2021}
}
MOTIVATION With the advent of high-throughput sequencing (HTS) in molecular biology and medicine, the need for scalable statistical solutions for modeling complex biological systems has become of critical importance. The increasing number of platforms and possible experimental scenarios raised the problem of integrating large amounts of new heterogeneous data and current knowledge, to test novel hypotheses and improve our comprehension of physiological processes and diseases. RESULTS… 

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