Prediction of protein–protein interactions based on elastic net and deep forest
نویسندگان
چکیده
Prediction of protein–protein interactions (PPIs) helps to grasp molecular roots disease. However, web-lab experiments predict PPIs are limited and costly. Using machine-learning-based frameworks can not only automatically identify PPIs, but also provide new ideas for drug research development from a promising alternative. We present novel deep-forest-based method prediction. Firstly, pseudo amino acid composition (PAAC), autocorrelation descriptor (Auto), multivariate mutual information (MMI), composition-transition-distribution (CTD), position-specific scoring matrix (AAC-PSSM), dipeptide PSSM (DPC-PSSM) adopted extract construct the pattern PPIs. Secondly, elastic net is utilized optimize initial feature vectors boost predictive performance. Finally, we ensemble XGBoost, random forest, extremely randomized trees deep forest model via cascade architecture prediction (GcForest-PPI). Benchmark reveal that proposed approach outperforms other state-of-the-art predictors on Saccharomyces cerevisiae Helicobacter pylori. apply GcForest-PPI independent test sets, CD9-core network, crossover cancer-specific network. The evaluation shows accuracy, complement improve discovery.
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ژورنال
عنوان ژورنال: Expert Systems With Applications
سال: 2021
ISSN: ['1873-6793', '0957-4174']
DOI: https://doi.org/10.1016/j.eswa.2021.114876