JUPPI: A Multi-Level Feature Based Method for PPI Prediction and a Refined Strategy for Performance Assessment

Over the years, several methods have been proposed for the computational PPI prediction with different performance evaluation strategies. While attempting to benchmark performance scores, most of these methods often suffer with ill-treated cross-validation strategies, adhoc selection of positive/negative samples etc. To address these issues, in our proposed multi-level feature based PPI prediction approach (
JUPPI
), using sequence, domain and GO information as features, a refined evaluation strategy has been introduced. During the evaluation process, we first extract high quality negative data using three-stage filtering, and then introduce a pair-input based cross validation strategy with three difficulty levels for test-set predictions. Our proposed evaluation strategy reduces the component-level overlapping issue in test sets. Performance of
JUPPI
is compared with those of the
state-of-the-art
approaches in this domain and tested on six independent PPI datasets. In almost all the datasets,
JUPPI
outperforms the
state-of-the-art
not only at human proteome level for PPI prediction, but also for prediction of interactors for intrinsic disordered human proteins.
https://figshare.com/projects/JUPPI_A_Multi-level_Feature_Based_Method_for_PPI_Prediction_and_a_Refined_Strategy_for_Performance_Assessment/81656

JUPPI
tool and the developed datasets (
JUPPId
) are available in public domain for academic use along with supplementary materials, which can be found on the Computer Society Digital Library at
http://doi.ieeecomputersociety.org/10.1109/TCBB.2020.3004970
.