ArticlePloS one2017
Prediction of N-linked glycosylation sites using position relative features and statistical moments.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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
34 citing papers in PubMed, 1 synthesis or guideline pooled it, 98 citations in OpenAlex.
- A systematic review of recent trends in research on therapeutically significant L-asparaginase and acute lymphoblastic leukemia.Molecular biology reports · 2022Pooled it
- Stress on the Endoplasmic Reticulum Impairs the Photosynthetic Efficiency of Chlamydomonas.International journal of molecular sciences · 2024Article
- ESM-BBB-Pred: a fine-tuned ESM 2.0 and deep neural networks for the identification of blood-brain barrier peptides.Briefings in bioinformatics · 2024Article
- DEL-Thyroid: deep ensemble learning framework for detection of thyroid cancer progression through genomic mutation.BMC medical informatics and decision making · 2024Article
- Predictive modeling for ubiquitin proteins through advanced machine learning technique.Heliyon · 2024Article
- Combining machine learning with structure-based protein design to predict and engineer post-translational modifications of proteins.PLoS computational biology · 2024Article
- An ensemble-based deep learning model for detection of mutation causing cutaneous melanoma.Scientific reports · 2023Article
- A Framework for Prediction of Oncogenomic Progression Aiding Personalized Treatment of Gastric Cancer.Diagnostics (Basel, Switzerland) · 2023Article
- RCCC_Pred: A Novel Method for Sequence-Based Identification of Renal Clear Cell Carcinoma Genes through DNA Mutations and a Blend of Features.Diagnostics (Basel, Switzerland) · 2022Article
- Artificial intelligence in the analysis of glycosylation data.Biotechnology advances · 2022Review
- Deep Learning Approaches for Detection of Breast Adenocarcinoma Causing Carcinogenic Mutations.International journal of molecular sciences · 2022Article
- A machine learning technique for identifying DNA enhancer regions utilizing CIS-regulatory element patterns.Scientific reports · 2022Article
- Immunoglobulin G N-glycan Biomarkers for Autoimmune Diseases: Current State and a Glycoinformatics Perspective.International journal of molecular sciences · 2022Review
- iAcety-SmRF: Identification of Acetylation Protein by Using Statistical Moments and Random Forest.Membranes · 2022Article
- An analytical study on the identification of N-linked glycosylation sites using machine learning model.PeerJ. Computer science · 2022Article
- Maximizing Depth of PTM Coverage: Generating Robust MS Datasets for Computational Prediction Modeling.Methods in molecular biology (Clifton, N.J.) · 2022Review
- Identification of stress response proteins through fusion of machine learning models and statistical paradigms.Scientific reports · 2021Article
- Evaluating machine learning methodologies for identification of cancer driver genes.Scientific reports · 2021Article
- iTAGPred: A Two-Level Prediction Model for Identification of Angiogenesis and Tumor Angiogenesis Biomarkers.Applied bionics and biomechanics · 2021Article
- iSUMOK-PseAAC: prediction of lysine sumoylation sites using statistical moments and Chou's PseAAC.PeerJ · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Glycosylation is one of the most complex post translation modification in eukaryotic cells. Almost 50% of the human proteome is glycosylated as glycosylation plays a vital role in various biological functions such as antigen's recognition, cell-cell communication, expression of genes and protein folding. It is a significant challenge to identify glycosylation sites in protein sequences as experimental methods are time taking and expensive. A reliable computational method is desirable for the identification of glycosylation sites. In this study, a comprehensive technique for the identification of N-linked glycosylation sites has been proposed using machine learning. The proposed predictor was trained using an up-to-date dataset through back propagation algorithm for multilayer neural network. The results of ten-fold cross-validation and other performance measures such as accuracy, sensitivity, specificity and Mathew's correlation coefficient inferred that the accuracy of proposed tool is far better than the existing systems such as Glyomine, GlycoEP, Ensemble SVM and GPP.
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.