ArticleComputational and structural biotechnology journal2022
DeepMC-iNABP: Deep learning for multiclass identification and classification of nucleic acid-binding proteins.
Article in Computational and structural biotechnology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed.
- Predicting protein-protein interaction sites based on dynamic perception mechanism within a hierarchical E(n)-equivariant graph.Briefings in bioinformatics · 2026Article
- caRBP-Pred: Leveraging Protein Language Models for the Prediction of Chromatin-Associated RNA-Binding Proteins.Computational and structural biotechnology journal · 2026Article
- In silico discovery of umami peptides: From sequence-based intelligent models to structure-function mechanistic insight.Current research in food science · 2026Review
- GTE-PPIS: a protein-protein interaction site predictor based on graph transformer and equivariant graph neural network.Briefings in bioinformatics · 2025Article
- DPNN-ac4C: a dual-path neural network with self-attention mechanism for identification of N4-acetylcytidine (ac4C) in mRNA.Bioinformatics (Oxford, England) · 2024Article
- MultiFeatVotPIP: a voting-based ensemble learning framework for predicting proinflammatory peptides.Briefings in bioinformatics · 2024Article
- PMTPred: machine-learning-based prediction of protein methyltransferases using the composition of k-spaced amino acid pairs.Molecular diversity · 2024Article
- iNP_ESM: Neuropeptide Identification Based on Evolutionary Scale Modeling and Unified Representation Embedding Features.International journal of molecular sciences · 2024Article
- EPDRNA: A Model for Identifying DNA-RNA Binding Sites in Disease-Related Proteins.The protein journal · 2024Article
- Using the Random Forest for Identifying Key Physicochemical Properties of Amino Acids to Discriminate Anticancer and Non-Anticancer Peptides.International journal of molecular sciences · 2023Article
- A Machine Learning Method to Identify Umami Peptide Sequences by Using Multiplicative LSTM Embedded Features.Foods (Basel, Switzerland) · 2023Article
- An Optimized Ensemble Deep Learning Model for Predicting Plant miRNA-IncRNA Based on Artificial Gorilla Troops Algorithm.Sensors (Basel, Switzerland) · 2023Article
- A systematic pan-cancer analysis reveals the clinical prognosis and immunotherapy value of C-X3-C motif ligand 1 (CX3CL1).Frontiers in genetics · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nucleic acid-binding proteins (NABPs), including DNA-binding proteins (DBPs) and RNA-binding proteins (RBPs), play vital roles in gene expression. Accurate identification of these proteins is crucial. However, there are two existing challenges: one is the problem of ignoring DNA- and RNA-binding proteins (DRBPs), and the other is a cross-predicting problem referring to DBP predictors predicting DBPs as RBPs, and vice versa. In this study, we proposed a computational predictor, called DeepMC-iNABP, with the goal of solving these difficulties by utilizing a multiclass classification strategy and deep learning approaches. DBPs, RBPs, DRBPs and non-NABPs as separate classes of data were used for training the DeepMC-iNABP model. The results on test data collected in this study and two independent test datasets showed that DeepMC-iNABP has a strong advantage in identifying the DRBPs and has the ability to alleviate the cross-prediction problem to a certain extent. The web-server of DeepMC-iNABP is freely available at http://www.deepmc-inabp.net/. The datasets used in this research can also be downloaded from the website.
Indexed as
Identifiers
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.