ArticleMolecules (Basel, Switzerland)2018
Prediction of Protein-Protein Interactions from Amino Acid Sequences Based on Continuous and Discrete Wavelet Transform Features.
Article in Molecules (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Recent advances in deep learning for protein-protein interaction: a review.BioData mining · 2025Review
- DL-PPI: a method on prediction of sequenced protein-protein interaction based on deep learning.BMC bioinformatics · 2023Article
- ProtInteract: A deep learning framework for predicting protein-protein interactions.Computational and structural biotechnology journal · 2023Article
- Predicting Protein-Protein Interactions Based on Ensemble Learning-Based Model from Protein Sequence.Biology · 2022Article
- Protein-protein interaction prediction with deep learning: A comprehensive review.Computational and structural biotechnology journal · 2022Review
- Protein-Protein Interactions Prediction Based on Graph Energy and Protein Sequence Information.Molecules (Basel, Switzerland) · 2020Article
- Prediction of Protein-Protein Interactions with Local Weight-Sharing Mechanism in Deep Learning.BioMed research international · 2020Article
- Recognizing ion ligand binding sites by SMO algorithm.BMC molecular and cell biology · 2019Article
- Prediction of Protein-Protein Interactions Based on Domain.Computational and mathematical methods in medicine · 2019Article
- Deep Neural Network Based Predictions of Protein Interactions Using Primary Sequences.Molecules (Basel, Switzerland) · 2018Article
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Authors and funding
6 authors.
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Abstract
Protein-protein interactions (PPIs) play important roles in various aspects of the structural and functional organization of cells; thus, detecting PPIs is one of the most important issues in current molecular biology. Although much effort has been devoted to using high-throughput techniques to identify protein-protein interactions, the experimental methods are both time-consuming and costly. In addition, they yield high rates of false positive and false negative results. In addition, most of the proposed computational methods are limited in information about protein homology or the interaction marks of the protein partners. In this paper, we report a computational method only using the information from protein sequences. The main improvements come from novel protein sequence representation by combing the continuous and discrete wavelet transforms and from adopting weighted sparse representation-based classifier (WSRC). The proposed method was used to predict PPIs from three different datasets: yeast, human and
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