ArticleCurrent genomics2020
Extremely-randomized-tree-based Prediction of N
Article in Current genomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Causal Relationship Between Hypertension And Vertigo: A Mendelian Randomization Study.Current neurovascular research · 2025Article
- Ranking Breast Cancer Drugs and Biomarkers Identification Using Machine Learning and Pharmacogenomics.ACS pharmacology & translational science · 2023Article
- Machine learning-driven exploration of drug therapies for triple-negative breast cancer treatment.Frontiers in molecular biosciences · 2023Article
- Deep-4mCGP: A Deep Learning Approach to Predict 4mC Sites inInternational journal of molecular sciences · 2022Article
- Comparative analysis of machine learning-based approaches for identifying therapeutic peptides targeting SARS-CoV-2.Briefings in bioinformatics · 2022Article
- SortPred: The first machine learning based predictor to identify bacterial sortases and their classes using sequence-derived information.Computational and structural biotechnology journal · 2022Article
- Identification ofComputational and mathematical methods in medicine · 2022Article
- Artificial Intelligence, Machine Learning and Deep Learning in Ion Channel Bioinformatics.Membranes · 2021Review
- Machine learning, transcriptome, and genotyping chip analyses provide insights into SNP markers identifying flower color in Platycodon grandiflorus.Scientific reports · 2021Article
- Rheumatoid Arthritis and Cardio-Cerebrovascular Disease: A Mendelian Randomization Study.Frontiers in genetics · 2021Article
- Fast and Accurate Classification of Meta-Genomics Long Reads With deSAMBA.Frontiers in cell and developmental biology · 2021Article
- Recent Development of Machine Learning Methods in Microbial Phosphorylation Sites.Current genomics · 2020Review
- Computational RNA Epigenetics.Current genomics · 2020Article
- T4SE-XGB: Interpretable Sequence-Based Prediction of Type IV Secreted Effectors Using eXtreme Gradient Boosting Algorithm.Frontiers in microbiology · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionN METHODOLOGY: In this study, we proposed a novel computational predictor termed ERT-m6Apred, for the accurate prediction of m6A sites. To identify the feature encodings with more discriminative capability, we applied a two-step feature selection technique on seven different feature encodings and identified the corresponding optimal feature set.
resultsSubsequently, performance comparison of the corresponding optimal feature set-based extremely randomized tree model revealed that Pseudo k-tuple composition encoding, which includes 14 physicochemical properties significantly outperformed other encodings. Moreover, ERT-m6Apred achieved an accuracy of 78.84% during cross-validation analysis, which is comparatively better than recently reported predictors.
conclusionIn summary, ERT-m6Apred predicts
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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.