ReviewSN computer science2022
A Survey on Deep Networks Approaches in Prediction of Sequence-Based Protein-Protein Interactions.
Review in SN computer science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- IDBSpred: An intrinsically disordered binding site predictor using machine learning and protein language model.bioRxiv : the preprint server for biology · 2026Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The prominence of protein-protein interactions (PPIs) in system biology with diverse biological procedures has become the topic to discuss because it acts as a fundamental part in predicting the protein function of the target protein and drug ability of molecules. Numerous researches have been published to predict PPIs computationally because they provide an alternative solution to laboratory trials and a cost-effective way of predicting the most likely set of interactions at the entire proteome scale. In recent computational methods, deep learning has become a buzzword with numerous scientific researches. This paper presents, for the first time, a comprehensive survey of sequence-based PPI prediction by three popular deep learning architectures i.e. deep neural networks, convolutional neural networks and recurrent neural networks and its variants. The thorough survey discussed herein carefully mined every possible information, can help the researchers to further explore the success in this area.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.