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Resources » Paper

Dick K et al. (2020) Sci Rep "PIPE4: Fast PPI Predictor for Comprehensive Inter- and Cross-Species Interactomes."

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  • Comments on Dick K et al. (2020) Sci Rep "PIPE4: Fast PPI Predictor for Comprehensive Inter- and Cross-Species Interactomes." (0)

  • Overview

    PMID:
    31996697
    DOI:
    10.1038/s41598-019-56895-w
    Status:
    Publication type:
    Journal_article
    WormBase ID:
    WBPaper00059313

    Dick K, Samanfar B, Barnes B, Cober ER, Mimee B, Tan LH, Molnar SJ, Biggar KK, Golshani A, Dehne F, & Green JR (2020). PIPE4: Fast PPI Predictor for Comprehensive Inter- and Cross-Species Interactomes. Sci Rep, 10, 1390. doi:10.1038/s41598-019-56895-w

    The need for larger-scale and increasingly complex protein-protein interaction (PPI) prediction tasks demands that state-of-the-art predictors be highly efficient and adapted to inter- and cross-species predictions. Furthermore, the ability to generate comprehensive interactomes has enabled the appraisal of each PPI in the context of all predictions leading to further improvements in classification performance in the face of extreme class imbalance using the Reciprocal Perspective (RP) framework. We here describe the PIPE4 algorithm. Adaptation of the PIPE3/MP-PIPE sequence preprocessing step led to upwards of 50x speedup and the new Similarity Weighted Score appropriately normalizes for window frequency when applied to any inter- and cross-species prediction schemas. Comprehensive interactomes for three prediction schemas are generated: (1) cross-species predictions, where Arabidopsis thaliana is used as a proxy to predict the comprehensive Glycine max interactome, (2) inter-species predictions between Homo sapiens-HIV1, and (3) a combined schema involving both cross- and inter-species predictions, where both Arabidopsis thaliana and Caenorhabditis elegans are used as proxy species to predict the interactome between Glycine max (the soybean legume) and Heterodera glycines (the soybean cyst nematode). Comparing PIPE4 with the state-of-the-art resulted in improved performance, indicative that it should be the method of choice for complex PPI prediction schemas.

    Authors: Dick K, Samanfar B, Barnes B, Cober ER, Mimee B, Tan LH, Molnar SJ, Biggar KK, Golshani A, Dehne F, Green JR


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