Predicting Protein-Protein Interactions with Pseudo Amino Acid Composition

2008 ◽  
Author(s):  
Yu-Dong Cai ◽  
Guo-Ping Zhou
Keyword(s):  
Amino Acid ◽  
Amino Acid Composition ◽  
Acid Composition ◽  
Protein Interactions ◽  
Protein Protein Interactions ◽  
Pseudo Amino Acid Composition

scholarly journals Exploiting Amino Acid Composition for Predicting Protein-Protein Interactions

PLoS ONE ◽  
2009 ◽  
Vol 4 (11) ◽  
pp. e7813 ◽  
Author(s):  
Sushmita Roy ◽  
Diego Martinez ◽  
Harriett Platero ◽  
Terran Lane ◽  
Margaret Werner-Washburne
Keyword(s):  
Amino Acid ◽  
Amino Acid Composition ◽  
Acid Composition ◽  
Protein Interactions ◽  
Protein Protein Interactions

scholarly journals Prediction of protein-protein interactions based on elastic net and deep forest

2020 ◽  
Author(s):  
Bin Yu ◽  
Cheng Chen ◽  
Zhaomin Yu ◽  
Anjun Ma ◽  
Bingqiang Liu ◽  
...  
Keyword(s):  
Amino Acid ◽  
Amino Acid Composition ◽  
Acid Composition ◽  
Protein Interactions ◽  
Predictive Performance ◽  
Elastic Net ◽  
Core Network ◽  
Protein Protein Interactions ◽  
Promising Alternative ◽  
Deep Forest

AbstractPrediction of protein-protein interactions (PPIs) helps to grasp molecular roots of disease. However, web-lab experiments to 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 and development from a promising alternative. We present a novel deep-forest-based method for PPIs prediction. First, pseudo amino acid composition (PAAC), autocorrelation descriptor (Auto), multivariate mutual information (MMI), composition-transition-distribution (CTD), and amino acid composition PSSM (AAC-PSSM), and dipeptide composition PSSM (DPC-PSSM) are adopted to extract and construct the pattern of PPIs. Secondly, elastic net is utilized to optimize the initial feature vectors and boost the predictive performance. Finally, GcForest-PPI model based on deep forest is built up. Benchmark experiments reveal that the accuracy values of Saccharomyces cerevisiae and Helicobacter pylori are 95.44% and 89.26%. We also apply GcForest-PPI on independent test sets and CD9-core network, crossover network, and cancer-specific network. The evaluation shows that GcForest-PPI can boost the prediction accuracy, complement experiments and improve drug discovery. The datasets and code of GcForest-PPI could be downloaded at https://github.com/QUST-AIBBDRC/GcForest-PPI/.

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