Published May 20, 2020 | Version 1
Dataset Open

Within and cross species predictions of plant specialized metabolism genes using transfer learning

  • 1. Department of Plant Biology and Ecology, Evolutionary Biology, and Behavior program, Michigan State University; Department of Botany, University of Wisconsin-Madison
  • 2. Department of Plant Biology, Michigan State University
  • 3. Department of Biochemistry and Molecular Biology,Michigan State University
  • 4. Department of Biology, The College of New Jersey
  • 5. Department of Biochemistry and Molecular Biology, Michigan State University
  • 6. MSU-DOE Plant Research Laboratory, Michigan State University; Science Research Center, Yamaguchi University
  • 7. Department of Plant Biology and Biochemistry and Molecular Biology, Michigan State University
  • 8. Department of Horticulture, Michigan State University
  • 9. Department of Plant Biology, Ecology, Evolutionary Biology, and Behavior program, and Department of Computational Mathematics, Science and Engineering, Michigan State University

Description

Datasets for Within and cross species predictions of plant specialized metabolism genes using transfer learning. 

  1. Dataset 1: All gene features used in the full feature machine learning models including expression, co-expression, evolutionary, duplication, and protein domain features.
  2. Dataset 2: All gene features used in shared feature machine learning models including expression, evolutionary, duplication, and protein domain features.
  3. Table S1: All gene annotations from TomatoCyc or manual annotation.
  4. Table S2: All model scores.
  5. Table S3: All gene scores and predictions from each model.
  6. Table S4: Feature importance for 5 models.
  7. Table S5: Statistical analysis between classes for binary and continuous feature data.
  8. Table S6: RNAseq datasets used in analysis.

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Additional details

References

  • Within and cross species predictions of plant specialized metabolism genes using transfer learning Bethany M. Moore, Peipei Wang, Pengxiang Fan, Aaron Lee, Bryan Leong, Yann-Ru Lou, Craig A. Schenck, Koichi Sugimoto, Robert Last, Melissa D. Lehti-Shiu, Cornelius S. Barry, Shin-Han Shiu bioRxiv 2020.01.13.112102; doi: https://doi.org/10.1101/2020.01.13.112102