ArticlePloS one2019
A novel matrix of sequence descriptors for predicting protein-protein interactions from amino acid sequences.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- DensePPI-2: a bio-inspired update for sequence-based PPI prediction leveraging mutation rates.Briefings in bioinformatics · 2025Article
- Omics Data and Data Representations for Deep Learning-Based Predictive Modeling.International journal of molecular sciences · 2022Review
- Classifying COVID-19 based on amino acids encoding with machine learning algorithms.Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society · 2022Article
- A Survey on Deep Networks Approaches in Prediction of Sequence-Based Protein-Protein Interactions.SN computer science · 2022Review
- A deep learning model for plant lncRNA-protein interaction prediction with graph attention.Molecular genetics and genomics : MGG · 2020Article
- Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein-protein interactions (PPIs) play an important role in the life activities of organisms. With the availability of large amounts of protein sequence data, PPIs prediction methods have attracted increasing attention. A variety of protein sequence coding methods have emerged, but the training of these methods is particularly time consuming. To solve this issue, we have proposed a novel matrix sequence coding method. Based on deep neural network (DNN) and a novel matrix protein sequence descriptor, we constructed a protein interaction prediction model for predicting PPIs. When performed on human PPIs data, the method achieved an accuracy of 94.34%, a recall of 98.28%, an area under the curve (AUC) of 97.79% and a loss of 23.25%. A non-redundant dataset was used to evaluate this prediction model, and the prediction accuracy is 88.29%. These results indicate that the matrix of sequence (MOS) descriptor can enhance the predictive power of PPIs and reduce training time, which can be a useful complement for future proteomics research. The experimental code and experimental results can be found at https://github.com/smalltalkman/hppi-tensorflow.
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Registered trials
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