ArticleInternational journal of molecular sciences2017
Protein-Protein Interactions Prediction Using a Novel Local Conjoint Triad Descriptor of Amino Acid Sequences.
Article in International journal of molecular sciences, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
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- Computational Design and Optimization of Peptide Inhibitors for SIRT2.Pharmaceuticals (Basel, Switzerland) · 2024Article
- TUnA: an uncertainty-aware transformer model for sequence-based protein-protein interaction prediction.Briefings in bioinformatics · 2024Article
- Cracking the black box of deep sequence-based protein-protein interaction prediction.Briefings in bioinformatics · 2024Article
- Determining human-coronavirus protein-protein interaction using machine intelligence.Medicine in novel technology and devices · 2023Article
- Protein-protein interaction and non-interaction predictions using gene sequence natural vector.Communications biology · 2022Article
- SDNN-PPI: self-attention with deep neural network effect on protein-protein interaction prediction.BMC genomics · 2022Article
- LSTM-PHV: prediction of human-virus protein-protein interactions by LSTM with word2vec.Briefings in bioinformatics · 2021Article
- Computational predictions for protein sequences of COVID-19 virus via machine learning algorithms.Medical & biological engineering & computing · 2021Article
- PRPI-SC: an ensemble deep learning model for predicting plant lncRNA-protein interactions.BMC bioinformatics · 2021Article
- Predicting enzymatic reactions with a molecular transformer.Chemical science · 2021Article
- Graph2GO: a multi-modal attributed network embedding method for inferring protein functions.GigaScience · 2020Article
- Identifying Heat Shock Protein Families from Imbalanced Data by Using Combined Features.Computational and mathematical methods in medicine · 2020Article
- Identification of infectious disease-associated host genes using machine learning techniques.BMC bioinformatics · 2019Article
- Exploring the Potential of Spherical Harmonics and PCVM for Compounds Activity Prediction.International journal of molecular sciences · 2019Article
- mACPpred: A Support Vector Machine-Based Meta-Predictor for Identification of Anticancer Peptides.International journal of molecular sciences · 2019Article
- A novel matrix of sequence descriptors for predicting protein-protein interactions from amino acid sequences.PloS one · 2019Article
- Predicting protein-protein interactions using high-quality non-interacting pairs.BMC bioinformatics · 2018Article
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Protein-protein interactions (PPIs) play crucial roles in almost all cellular processes. Although a large amount of PPIs have been verified by high-throughput techniques in the past decades, currently known PPIs pairs are still far from complete. Furthermore, the wet-lab experiments based techniques for detecting PPIs are time-consuming and expensive. Hence, it is urgent and essential to develop automatic computational methods to efficiently and accurately predict PPIs. In this paper, a sequence-based approach called DNN-LCTD is developed by combining deep neural networks (DNNs) and a novel local conjoint triad description (LCTD) feature representation. LCTD incorporates the advantage of local description and conjoint triad, thus, it is capable to account for the interactions between residues in both continuous and discontinuous regions of amino acid sequences. DNNs can not only learn suitable features from the data by themselves, but also learn and discover hierarchical representations of data. When performing on the PPIs data of
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