ArticleGenomics, proteomics & bioinformatics2018
Integration of A Deep Learning Classifier with A Random Forest Approach for Predicting Malonylation Sites.
Article in Genomics, proteomics & bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Current computational tools for protein lysine acylation site prediction.Briefings in bioinformatics · 2024Pooled it
- Artificial intelligence in plant salt stress research: from predictive models to multi-omics integration.Journal of experimental botany · 2026Review
- Integrative transcriptomic and machine learning analysis identifies key extracellular matrix-related genes in diabetic retinopathy.Scientific reports · 2026Article
- CT Radiomics-based machine learning approach for the invasiveness of pulmonary ground-glass nodules prediction.European journal of radiology open · 2025Article
- A Machine Learning Model for Predicting Intensive Care Unit Admission in Inpatients with COVID-19 Using Clinical Data and Laboratory Biomarkers.Biomedicines · 2025Article
- Integrating deep learning features from mammography with SHAP values for a machine learning model predicting over 5-year recurrence of breast ductal carcinomaFrontiers in immunology · 2025Article
- CT-based radiomics integrated model for brain metastases in stage III/IV ALK-positive lung adenocarcinoma patients.Frontiers in oncology · 2025Article
- Analysis and review of techniques and tools based on machine learning and deep learning for prediction of lysine malonylation sites in protein sequences.Database : the journal of biological databases and curation · 2024Review
- PSATF-6mA: an integrated learning fusion feature-encoded DNA-6 mA methylcytosine modification site recognition model based on attentional mechanisms.Frontiers in genetics · 2024Article
- Compositional features analysis by machine learning in genome represents linear adaptation of monkeypox virus.Frontiers in genetics · 2024Article
- Machine learning-based models for the prediction of breast cancer recurrence risk.BMC medical informatics and decision making · 2023Article
- ResNetKhib: a novel cell type-specific tool for predicting lysine 2-hydroxyisobutylation sites via transfer learning.Briefings in bioinformatics · 2023Article
- ACP-GBDT: An improved anticancer peptide identification method with gradient boosting decision tree.Frontiers in genetics · 2023Article
- Article
- iFeatureOmega: an integrative platform for engineering, visualization and analysis of features from molecular sequences, structural and ligand data sets.Nucleic acids research · 2022Article
- A hybrid feature extraction scheme for efficient malonylation site prediction.Scientific reports · 2022Article
- Non-linearity of Metabolic Pathways Critically Influences the Choice of Machine Learning Model.Frontiers in artificial intelligence · 2022Article
- Systematic Characterization of Lysine Post-translational Modification Sites Using MUscADEL.Methods in molecular biology (Clifton, N.J.) · 2022Article
- CNNArginineMe: A CNN structure for training models for predicting arginine methylation sites based on the One-Hot encoding of peptide sequence.Frontiers in genetics · 2022Article
- Knowledge structure and emerging trends in the application of deep learning in genetics research: A bibliometric analysis [2000-2021].Frontiers in genetics · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As a newly-identified protein post-translational modification, malonylation is involved in a variety of biological functions. Recognizing malonylation sites in substrates represents an initial but crucial step in elucidating the molecular mechanisms underlying protein malonylation. In this study, we constructed a deep learning (DL) network classifier based on long short-term memory (LSTM) with word embedding (LSTM
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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.