ArticleEvolutionary bioinformatics online2020
Predicting Self-Interacting Proteins Using a Recurrent Neural Network and Protein Evolutionary Information.
Article in Evolutionary bioinformatics online, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Protein Self-Interaction in Cellular Function and Network Evolution: Molecular Mechanisms, AI-Driven Insights, and Therapeutic Potentials.International journal of molecular sciences · 2026Review
- Robust and accurate prediction of self-interacting proteins from protein sequence information by exploiting weighted sparse representation based classifier.BMC bioinformatics · 2022Article
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4 authors.
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Abstract
Self-interacting proteins (SIPs) play crucial roles in biological activities of organisms. Many high-throughput methods can be used to identify SIPs. However, these methods are both time-consuming and expensive. How to develop effective computational approaches for identifying SIPs is a challenging task. In the article, we present a novel computational method called RRN-SIFT, which combines the recurrent neural network (RNN) with scale invariant feature transform (SIFT) to predict SIPs based on protein evolutionary information. The main advantage of the proposed RNN-SIFT model is that it uses SIFT for extracting key feature by exploring the evolutionary information embedded in Position-Specific Iterated BLAST-constructed position-specific scoring matrix and employs an RNN classifier to perform classification based on extracted features. Extensive experiments show that the RRN-SIFT obtained average accuracy of 94.34% and 97.12% on the
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